How skillfully can we simulate drivers of aerosol direct climate forcing at the regional scale ?

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Introduction and objectives
Atmospheric aerosol particles (aerosols) play a major role in dictating Earth's climate by both directly interacting with solar radiation (direct effect) and acting as cloud condensation nuclei and thus changing cloud properties (indirect effect) (Boucher et al., Introduction Conclusions References Tables Figures

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Full 2013).The global mean aerosol direct effect is estimated to be −0.27(possible range of −0.77 to +0.23) W m −2 , while the indirect effect is −0.55 (−1.33 to −0.06) W m −2 (Stocker et al., 2013).Therefore their combined radiative forcing is likely a significant fraction of the overall net anthropogenic climate forcing since pre-industrial times (i.e.1.13-3.33W m −2 , Stocker et al., 2013) and a substantial source of uncertainty in quantifying anthropogenic radiative forcing.Accurate quantification of direct aerosol radiative forcing is strongly dependent on aerosol precursor and primary aerosol emissions.Both have evolved over the past two decades in terms of their spatio-temporal distribution and absolute magnitude.Emissions have generally increased in emerging economies (Kurokawa et al., 2013), biogenic and anthropogenic emissions have altered in response to changing land use and land cover (Wu et al., 2012), and the implementation of pollution control strategies particularly in North America and Europe have resulted in declines in air pollutant emissions (Xing et al., 2015;Giannouli et al., 2011).Therefore there is evidence that aerosol burdens and thus direct climate forcing has varied markedly in the past and may change substantially in the future.Further, although best estimates of global anthropogenic radiative forcing from the aerosol direct and indirect effect are −0.27 and −0.55 W m −2 (Stocker et al., 2013) respectively, the short residence time and high spatio-temporal variability of aerosol populations mean their impact on regional climates can be much larger than the global mean but are even more uncertain.
Long-term continuous and high precision measurements of aerosol properties are largely confined to aerosol mass (total, PM 10 or PM 2.5 ) in the near-surface layer which may or may not be representative of either the total atmospheric burden (Ford and Heald, 2013;Alston et al., 2012), or radiation extinction and hence climate forcing.Columnar remote sensing measurements of aerosol optical properties are available from a range of ground-based and satellite-borne instrumentation, but have only a relatively short period of record, are subject to non-zero measurement uncertainty (and bias), and under-sample the range of atmospheric conditions due to cloud masking and infrequent satellite overpasses.Therefore, regional and global models are most Introduction

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Full commonly used to quantify historical and contemporary aerosol direct radiative forcing based on simulated properties such as the aerosol optical depth (AOD) and Ångström exponent (AE) (Boucher et al., 2013).Most global models that include aerosol microphysics have been run at fairly coarse resolution (spatial resolution of the order of 1-2.5 • ) (Table 1) usually for periods of a few years.The resulting fields of AOD (and less frequently AE) have been evaluated relative to ground-based and satellite-borne remote sensing optical properties measurements (Table 1).However, aerosol populations (and dynamics) are known to exhibit higher spatial variability (and scales) than can be manifest in those models (Kulmala et al., 2011;Spracklen et al., 2010).Despite recent improvements in the sophistication of aerosol processes and properties within global models, there are still substantial regional and latitudinal discrepancies in both the magnitude of AOD and other aerosol properties which impact aerosol direct radiative forcing and the degree of model-to-model agreement (Myhre et al., 2013).The skill of these models in reproducing the spatio-temporal variability in the aerosol size distribution, composition, concentration and radiative properties is incompletely characterized.Accordingly, there is large model-to-model variability both in the global mean direct aerosol forcing and in the spatial distribution thereof (Kulmala et al., 2011;Myhre et al., 2013).Although a direct comparison between the studies summarized in Table 1 is inherently very difficult due to the different performance metrics reported, and variations in both the model resolution and aerosol descriptions, there is a consistent finding of high spatial variability in model bias, both in sign and magnitude.Correlation coefficients of monthly and seasonal mean AOD from model simulations vs. satellite-based measurements are typically in a range ∼ 0.6-0.8 both in global (Colarco et al., 2010;Lee et al., 2015) and regional (Nabat et al., 2015) simulations.However, these correlations are largely reflective of the ability of the models to capture the seasonal cycle and columnar aerosol properties from remote sensing and thus ignore substantial variability on the synoptic (Sullivan et al., 2015) and meso-scales (Anderson et al., 2003).A wider range of correlation coefficients are reported when comparisons are made to high fre-Introduction

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Full quency observations of AOD at the hourly/daily timescale both in global (Sič et al., 2015) and regional (Rea et al., 2015) simulations (r ∼ 0.3-0.8).The largest range of correlation coefficients ([−0.99,0.9]; Table 1) is reported when simulated AOD is compared with observations from the AErosol RObotic NETwork (AERONET), and appear to be function of temporal averaging, location of AERONET sites and model resolution.
Correlations between time series of simulated AE vs. AERONET observations are reported less frequently, and when conducted for monthly mean values range from ∼ 0.4 (Li et al., 2015) to ∼ 0.8 (Colarco et al., 2010).At least some of the variability in model skill, as indicated by the mutual variability with observations described by correlation coefficients, and model-to-model agreement shown in AeroCom Phase II may be attributable to variations in model resolution, differences in gas and particle phase parameterizations and aerosol descriptions.However, there are also variations in the way in which model skill is evaluated leading to ambiguity in terms of prioritizing future research directions.The direct effect remains poorly quantified at the regional scale, due to uncertainty in aerosol loading, uncertainty and spatio-temporal variability in aerosol physical properties (Colarco et al., 2014) and a relative paucity of rigorous model verification and validation exercises.Confidence in projections of possible future aerosol radiative forcing requires detailed assessment of skill in the current climate, and the need for and benefits of regional downscaling and/or use of high-resolution global models requires careful quantification.
Regional models represent an opportunity to assess if running higher resolution simulations over specific regions of interest improves the characterization of aerosol optical properties of relevance to direct radiative forcing.Assessment of value added (or lack thereof) from high resolution regional vs. global coarse resolution models is not quantifiable from prior studies alone.Although high-resolution simulations, comparable to those presented herein, have been run, they are over a small temporal and spatial domain (e.g., Tuccella et al., 2015), or lack quantitative assessment of aerosol optical properties (e.g., Tessum et al., 2014).Thus, quantification of the skill of high-resolution modeling of aerosol optical properties is presented here.Forthcoming work will include Figures

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Full direct comparison to coarser resolution simulations to quantify the value added (or lack thereof) from increased model resolution.
We evaluate the skill of state-of-the-art high-resolution regional model simulations of climate-relevant aerosol properties using a range of inferential statistics and investigate possible sources of discrepancies with observations.The impact of aerosols on climate and human health are strengthened under conditions of enhanced aerosol concentrations, thus it is necessary to study and diagnose causes of "extreme aerosol events" (Chu, 2004;Gkikas et al., 2012), and to evaluate the ability of numerical models to simulate their occurrence, intensity, spatial extent and location.Prior analyses of Level-3 (1 • resolution) MODIS AOD over the eastern half of North America have indicated the frequency of co-occurrence of extreme AOD values (> local 90th percentile) decreases to below 50 % at ∼ 150 km from a central grid cell located in southern Indiana, but is above that expected by random chance over almost all of eastern North America (Sullivan et al., 2015).Thus, our evaluation exercise also includes an analysis of the spatio-temporal coherence of extreme events.
We applied the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem version 3.6.1)at high resolution (12 × 12 km) over eastern North America during the year 2008, in the context of a pseudo type-2 downscaling exercise in which the high-resolution model is nested within reanalysis boundary conditions (Castro et al., 2005).The choice of this spatial resolution is taken in part to match the resolution of North American Mesoscale Model that is used for the meteorological lateral boundary conditions and to ensure we capture some mesoscale variability while remaining computationally feasible.
Our evaluation is designed to investigate spatio-temporal variability of aerosol optical properties (i.e.AOD and AE) in their mean and extreme values.Thus, we conduct our evaluation of the simulations using: 1. High-frequency, disjunct time series data from columnar point measurements at AERONET stations.Figures

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Full 2. Relatively high-resolution spatial data from lower frequency (once daily or lower) data from polar orbiting satellites (i.e.MODIS and MISR).
We also include intercomparison with daily mean PM 2.5 concentrations from 1230 surface stations.These data for 2008 were obtained from the US Environmental Protection Agency (EPA) AirData web site and represent all available outdoor near-surface 24 h mean PM 2.5 measurements in the model domain.Most of these stations report values on a 1 day in 3 schedule.We further evaluate the WRF-Chem simulations of a key meteorological parameter -precipitation -relative to observations from the Delaware gridded dataset (Matsuura and Willmott, 2009).This data set includes monthly accumulated precipitation data on a 0.5×0.5 • grid which is estimated by interpolating station observations from the Global Historical Climatology Network using the spherical version of Shepard's distance-weighting method (Shepard, 1968;Willmott et al., 1985).

WRF-Chem simulations
The Weather Research and Forecasting Model with coupled chemistry (WRF-Chem, version 3.6.1)(Grell et al., 2005;Fast et al., 2006) is used to simulate aerosol processes over eastern North America during the whole of 2008.The simulation domain comprises 300 × 300 grid points with 12 km resolution and is centered in southern Indiana (86 Full ensures availability of multiple sources of ground-and space-based measurements of aerosol properties for evaluation of the simulations. Table 2 provides details of the WRF-Chem simulations.In brief, we used 32 vertical levels up to 50 hPa with telescoping to allow for a good vertical resolution in the boundary layer (i.e.approximately 10 layers below 1 km for non-mountainous regions).Meteorological lateral boundary conditions are provided every 6 h from the North American Mesoscale Model (NAM) applied at 12 km resolution.The initial and boundary chemical conditions are based on output from the offline global chemical transport model MOZART-4 (Model for Ozone and Related chemical Tracers, version 4), driven by meteorology from NCEP/NCAR-reanalysis (Pfister et al., 2011;Emmons et al., 2010).Anthropogenic emissions are from the POET (Precursors of Ozone and their Effects in the Troposphere) and the EDGAR (Emissions Database for Global Atmospheric Research) databases.The land cover is specified based on the USGS 24-category data at 3.7 km resolution (Anderson et al., 1976).Anthropogenic point and area emissions at 4 km resolution are input hourly from the US National Emissions Inventory (NEI-05) (US-EPA, 2009) and specified for 19 vertical levels (see Fig. 1 for an overview of the primary aerosol emissions).Biogenic emissions of isoprene, monoterpenes, other biogenic VOC (OVOC), and nitrogen gas emissions from the soil are described as a function of simulated temperature and photosynthetic active radiation (for isoprene) using the model of Guenther (Guenther et al., 1993(Guenther et al., , 1994;;Simpson et al., 1995).Aerosol and gas phase chemistry are described using the second generation Regional Acid Deposition Model (RADM2) chemical mechanism (Stockwell et al., 1990) and the Modal Aerosol Dynamics Model for Europe (MADE) which incorporates the Secondary Organic Aerosol Model (SORGAM) (Ackermann et al., 1998;Schell et al., 2001).The correct characterization of aerosol optical properties is strongly related to model skill in describing particle composition and mixing state (Li et al., 2015;Curci et al., 2014).With this in mind, it is worthy of note that aerosol components are assumed to be internally mixed within each mode (although the composition differs by mode).For the Aitken and accumulation modes the median diameters are 10 and 70 nm with standard Introduction

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Full deviations of 1.6 and 2, respectively.The choice of a modal representation of aerosol size distribution is dictated by the high computational demand of more sophisticated approaches (e.g.sectional description of the aerosol size distribution) for long-term simulations.With the current settings, the 1 year run was completed without restart in 9.5 days (230 h) on the Cray XE6/XK7 supercomputer (Big Red II) owned by Indiana University using 256 processors distributed on 8 nodes, thus indicating feasibility of this configuration for climate scale simulations.Aerosol, and gas phase concentrations and meteorological properties are saved once hourly.AE from the WRF-Chem simulations is computed using: AE = ln AOD 400 nm AOD 600 nm ln 600 nm 400 nm . (1) AOD at wavelengths (λ) of 500 and 550 nm, for comparison with MODIS and MISR respectively, are derived using the Ångström power law: . (2) We investigated the wavelength dependence on AE calculation using λ at 300 and 1000 nm as proposed in (Kumar et al., 2014) and found that, although AOD estimates are independent on the wavelength range selected, AE 400−600 nm is systematically lower than AE 300−1000 nm .Analyses of AE reported in this study are obtained using wavelengths at 400 and 600 nm since they are closer to those used in AE satellite retrievals.

Remotely-sensed data
Consistent with previous research (Sect. 1 and Table 1), we evaluate the WRF-Chem simulations using four primary remote sensing products -three are drawn from instruments on the Aqua and Terra satellites, while the fourth is from ground-based radiometers operated as part of the AERONET network.The data sets are as follows: Introduction

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Full 1.The MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the polar-orbiting Terra (∼ 10 : 30 overpass local solar time, LST) and Aqua (∼ 13:30 LST) satellites.They have measured atmospheric aerosol optical properties since 2000 and 2002 respectively, with near-global daily coverage (Remer et al., 2005).Herein we use the Level 2 (L2; 10 km resolution) "darktarget" products of AOD at 550 nm and AE from 470-660 nm (Collection 5.1; Levy et al., 2010).The L2 AOD uncertainty is ±0.05 ± 0.15× AOD over land relative to global sun photometer measurements from AERONET.AE is retrieved with higher uncertainty, and tends to exhibit a bi-modality in retrieved values (Levy et al., 2010;Remer et al., 2005) (see Fig. S1 in the Supplement).For this reason where we compare WRF-Chem simulated AE with values from MODIS we treat AE as a binary variable, wherein AE < 1 is taken as representing coarse mode dominated aerosol populations and AE > 1 indicates fine mode dominated populations (Pereira et al., 2011;Valenzuela et al., 2014).
3. Ground-based sun-photometer measurements from 22 AErosol RObotic NETwork (AERONET) (Holben et al., 1998) stations are also used in this study (Fig. 1).This network is highly spatially inhomogeneous, but under cloud-free conditions the observations are available at multiple times during daylight hours.AOD is measured directly by the AERONET sun photometers at seven wavelengths (340,380,440,500,670,870, and 1020 nm) with high accuracy (i.e.AOD uncertainty of < 0.01 for λ > 440 nm, Holben et al., 2001).The Ångström Exponent (AE) is cal-Introduction

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Full culated for all available wavelengths within the AOD range.The AE 870-440 nm includes the 870, 670, 500 and 440 nm AOD data.Level-2 aerosol products from AERONET (i.e.cloud screened and quality assured) have been used extensively in satellite and model validation studies (including many of those summarized in Table 1) and are used herein.
To avoid the discontinuity in the MODIS retrieval algorithm due to different assumed aerosol types (Levy et al., 2007), we confine our analyses of model skill to longitudes east of 98

Statistical methods used in the model evaluation
The primary error metric of overall model performance used herein is the Mean Fractional Bias (Boylan and Russell, 2006): MFB is a useful model performance indicator since it equally weights positive and negative biases.It varies between +2 and −2 and has a value of zero for an ideal model.Introduction

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Full Where MFB is reported for WRF-Chem vs. MODIS or MISR, C m is the monthly mean AOD or AE simulated by WRF-Chem at a specific location, C 0 refers to the same quantify from MODIS or MISR (Table 3) and N is the sample size.Where MFB is reported in comparisons of WRF-Chem with AERONET, the monthly average in the model grid cell containing the AERONET site is compared with monthly averaged observations (C 0 ).
The evaluation of WRF-Chem simulations of AOD and AE relative to satellite retrievals (MODIS and MISR) is also summarized using Taylor diagrams (Taylor, 2001) produced from the monthly means for the grid cells with simultaneous data availability.Taylor diagrams synthesize three aspects of model skill focused on evaluations of the spatial fields of the parameter of interest.The correlation coefficient of the modeled vs. observed field which is expressed by the azimuthal position, the root mean squared difference which is proportional to the distance between a point and the reference point on the x axis (at 1, 0), and the ratio of simulated and observed spatial standard deviation which is proportional to the radial distance from the origin.
To investigate model performance at given locations through time, empirical quantilequantile (EQQ) plots are constructed using high frequency realizations of AOD and AE at individual locations (AERONET sites) relative to WRF-Chem values simulated in the grid cell containing the measurement site.EQQ plots are thus generated for each of the AERONET stations using all hours when there are simultaneous estimates available from the direct observations and from the numerical simulations.The advantage of EQQ plots is that they make no assumptions regarding the underlying form of the data, and can be readily used to determine which parts of the modeled distribution deviate from the observations (and thus fall away from a 1 : 1 line).
The validity of AE estimates is a function of both the absolute magnitude of AOD and the uncertainty in the wavelength dependent AOD.AE provides information regarding the relative abundance of fine to coarse particles.Thus, here we quantify the model skill in reproducing spatial patterns of fine and coarse mode particles observed by MODIS (Terra) by comparing the frequency distribution of AE lower and higher than 1 to distinguish populations dominated by coarse and fine aerosols respectively in WRF-Introduction

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Full Chem and MODIS (Valenzuela et al., 2014;Pereira et al., 2011).The choice of this threshold reflects the AE distribution.AE simulated by WRF-Chem generally conforms to a single normal distribution centered on 1 during January-April and on 1.3 from May-June to December; AERONET time series also tend to conform to a single mode, while MODIS estimates typically are bimodally distributed (see Fig. S1).A χ 2 -test is applied to assess if the frequency distribution of fine and coarse particles is the same between MODIS and WRF-Chem.We therefore consider the data in the form of a contingency table (Table 4) and compute the χ 2 statistic with one degree of freedom from: where O i is the frequency of observations of type i and E i is the expected frequency of type i which is computed as the product of the row total with the column total, divided by the total number of observations.Herein we apply a 99 % confidence limit to assess significance of the χ 2 statistic.
As described above, the impact of aerosols on climate and human health are strengthened under conditions of enhanced aerosol concentrations, thus two analyses were undertaken to evaluate the ability of the WRF-Chem simulations to represent extreme AOD values: 1. Evaluation of the spatial patterns of extreme events.Using daily estimates of AOD in each grid cell and month we identified the 75th percentile value across space (i.e.p75) as threshold for extreme AOD for WRF-Chem and MODIS separately.The Accuracy describes the fraction of grid cells co-identified as exceeding p75 or not in MODIS and WRF-Chem, and thus equally weights event and non-event conditions.In this application, where extreme is identified as the 75th percentile, a value of 0.5 would indicate none of the grid cells experiencing extreme events were reproduced by the model, while 1 would indicate perfect identification of events and non-events.The HR and TS metrics give "credit" only those grid cells identified as "extreme".For these metrics, a value of 0 indicates no correct identification of grid cells with extreme values, while a perfect model would exhibit a value of 1.

Evaluation of AOD
Overall WRF-Chem is positively biased relative to remotely-sensed AOD.The spatial MFB is 0.20 (0.14) when computed using all available MODIS measurements from Terra (Aqua) and 0.50 relative to data from the AERONET stations (Table 3).The sign of this bias is consistent across the entire simulation domain (Fig. 2).These results agree with findings from previous regional studies that have also shown an overestimation of AOD by WRF-Chem over eastern North America and Europe (i.e.regions dominated by sulfate aerosols), and underestimation in western US and most of the rest of the globe (Zhang et al., 2012;Colarco et al., 2010;Curci et al., 2014) (Table 1).Higher biases of WRF-Chem simulated annual mean AOD are found in the southern portion of the domain (Fig. 2) where the model also exhibits a positive bias in daily mean nearsurface PM 2.5 relative to observations from 1230 US EPA sites (see Fig. 3 and Fig. S2).
The MFB of WRF-Chem relative to MODIS estimates of AOD is lower than the MFB relative to most of the AERONET stations except for a few sites located along the coast, one polluted site in the northeast and a few land sites in the North/North-West (Fig. 2c  and 4a).This is possibly a result of an inability of the model to capture variations in aerosol optical properties occurring at a local scale (below the resolution of 12 km).However, the evaluation statistics for WRF-Chem relative to AERONET did not vary consistently with the classification of AERONET stations.Indeed, the mean MFB for AOD in coastal, polluted and land sites varies between 0.26 (coastal) and 0.67 (land), whereas for AE it varies between −0.72 (coastal) and −0.50 (land).Spatial patterns of monthly mean AOD show largest differences relative to MODIS during winter months in the southern states and near the coastlines, which show MFB up to 0.7, and lower spatial correlation (see Fig. 5a).This may be due to the larger uncertainty in MODIS retrievals near the coast (Anderson et al., 2013), the smaller sample size in the observations (particularly at high latitudes) during December to March or the lower overall AOD.Conversely, the spatial correlation is maximized over the summer 27325 Introduction

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Full (r = 0.5-0.7)for MODIS and August for MISR, when most data are available.The spatial variability of monthly mean AOD fields is also well simulated by WRF-Chem during the warm season (months May-August), as indicated by the ratio of the spatial standard deviation is close to 1.However, it is usually higher in MODIS and/or MISR than in WRF-Chem.The RMSD is largest and the spatial correlation is lowest during September and October, when MFB is also > 0.4 in part because WRF-Chem simulates high AOD and aerosol nitrate and sulfate concentrations over large regions in eastern North America.The high positive bias in these months is also reflected in the near-surface PM 2.5 (Fig. S2).A possible explanation for the relatively poor model performance during September and October may derive from the simulation of precipitation.During the majority of calendar months, domain averaged precipitation as simulated by WRF-Chem is slightly positively biased relative to the gridded observational data.However, during September and October, the model exhibits a negative bias (of 8-10 % relative to observations) and substantial underestimation of precipitation in regions of typically high AOD such as the Ohio River valley and along the east coast (Fig. S3).
Empirical quantile-quantile plots of AOD at AERONET stations computed for both simultaneous MODIS observations and WRF-Chem with AERONET observations indicate that the positive bias in WRF-Chem simulated values of AOD is evident across much of the probability distribution (5th to 95th percentile values) at most AERONET stations.However, it is worthy of note that WRF-Chem comparisons with AERONET observations occupy much of the same parameter space as simultaneous MODIS and AERONET observations at those sites (Fig. 6a).Thus, model simulations reproduce the range and probability of low-uncertainty AERONET measured AOD nearly as well as MODIS.

Evaluation of AE
As described above, AE is retrieved with much lower confidence than AOD from the MODIS measurements.Nevertheless, the correlation between WRF-Chem and MODIS monthly mean AE seems to be independent of season and lies between 0.28 27326 Introduction

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Full and 0.52 for all months except April, May and November when it is lower, whereas r is always < 0.25 when comparing with MISR (Fig. 5b).As for AOD, we computed the Spearman's rank correlation coefficient to reduce the possible bias due to few outliers and the smaller sample size in MISR data (N varies between 2300-5500 depending on the month and is approximately 5 times smaller than the sample size for MODIS).The AE RMSD relative to MODIS or MISR does not exhibit a clear seasonal pattern and the ratio of spatial standard deviations in the AE fields is always lower than 1, indicating more spatial variability in the satellite retrievals than in WRF-Chem.The degree to which these results are symptomatic of the difficulties in retrieving AE from the remote sensing observations is unclear.When the AE values are treated as binary samples (< 1 indicating coarse mode aerosols dominate, while AE > 1 indicating a dominance of the fine mode) and presented as a contingency table, WRF-Chem and MODIS simultaneously identify coarse mode dominance (i.e.AE < 1) in 18 % of grid cells (Table 5).
After cloud screening, WRF-Chem simulates 31 % of grid cells as exhibiting annual mean AE > 1, while MODIS indicates a larger fraction of grid cells with AE > 1 (80 %, Table 5).Both WRF-Chem and MODIS indicate the highest prevalence of fine mode particles during the warm months with highest agreement for co-identification (above 50 %) during June-September.Co-identification of coarse mode particles is highest in the winter and spring months (above 20 % during February-May and December, Table 5).However, when a χ 2 test is applied to the frequency of fine and coarse particles identified by WRF-Chem and MODIS, for all months except January and April, the p value is < 0.01, thus we reject the null hypothesis of equal distribution of fine and coarse mode particles identified by MODIS and WRF-Chem.The two data sets agree on 29 % of the cases when trying to identify fine mode particles and approximately 53 % of the cells are misclassified with MODIS usually identifying a high prevalence of fine aerosols than WRF-Chem.AE from WRF-Chem is also negatively biased relative to AERONET observations, with MFB = −0.59indicating WRF-Chem is simulating a greater prevalence of coarse mode aerosols (Table 3, Figs. 2 and 4b).EQQ plots for all sites show good accord between WRF-Chem and AERONET observations, as in-Introduction

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Full dicated by the relatively consistent fractional error across the entire range of simulated and observed AE (Fig. 6b).Simulations from previous studies have also shown a systematic negative bias of simulated AE vs. MODIS observations.Highest biases have been noted in regions dominated by dust aerosols or when the model overestimates the dust loading, since aerosol population mean diameter is inversely proportional to AE (Colarco et al., 2014;Balzarini et al., 2014).Sources of the biases in our study, include the simplified treatment of the size distribution, weaknesses in the emission inventory or uncertainties in meteorological variables affecting particle growth (e.g.temperature and relative humidity).Future work will focus on examining these sensitivities.

AOD extremes
Averaged across the entire simulation period, WRF-Chem correctly identifies 70 % of locations with extreme and non-extreme AOD in the MODIS observations (i.e. the Accuracy = 70 %, Table 6).The overall TS and HR also indicate the geographic location of extreme AOD is similar between the model and satellite retrievals.The annual mean HR, which is defined as the proportion of grid cells with extreme AOD co-identified by WRF-Chem and MODIS relative to MODIS extremes, is 41 %.The annual mean TS, which also takes into account false alarms, is 27 % (Table 6).
For each month, the HR is significantly higher than the probability of co-identification of extremes by random chance (i.e.p 0 = 0.25 2 = 0.0625), since the test statistic N is always larger than the critical value at 1 % (i.e.2.575).HR and TS vary seasonally, with highest skill during summer months (HR up to 70 % and TS up to 54 %), and lowest skill during winter and early spring (minimum HR = 29 % and minimum TS = 17 %) (Table 6 and Fig. 7).The relatively low skill in identifying the spatial occurrence of high AOD during winter and spring may reflect the relatively low AOD and low spatial variability during this season, which means "extreme" AOD may differ Introduction

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Full only marginally from the "non-extreme" areas (see Fig. S4 for monthly comparisons of extreme area identification).
The spatial distribution of extreme AOD also displays some seasonality with areas of AOD > p75 concentrated over coastal regions and the southern states during summer months and smaller areas during winter and early spring (Fig. 7).Despite the relatively low simultaneous identification of extremes during cold seasons, the location of extremes moves from the coast to the Great Lakes region and Midwest states in both the model and MODIS (see Fig. S3).During winter and spring months WRF-Chem simulates more areas with extreme AOD over coastal regions, whereas MODIS shows more spatial variability and predicts higher AOD in the Great Lakes area and in the states west of Illinois.Conversely, WRF-Chem underestimates areas of extreme AOD relative to MODIS in the northern regions of the domain, possibly due to the underestimation of sulfate-aerosol.These two observations may be explained noting that the mass fraction of aerosol nitrate in the accumulation and coarse mode predicted by WRF-Chem during most of fall and winter months dominates the sulfate fraction over virtually all of the domain (see Fig. S5), whereas point observations indicate aerosol nitrate mass fraction is dominant only over the Central Great Plains (Hand et al., 2012).This may be related to an overestimation of aerosol-nitrate as a result of the impact of air temperature and relative humidity on aerosol ammonium nitrate (NH 4 NO 3 ) stability (Aksoyoglu et al., 2011), as well as an underestimation of aerosol sulfate likely due to underestimation of the rate of SO 2 gaseous and aqueous (missing) oxidation, or underestimation of the nighttime boundary layer height which impacts sulfate formation near the surface (Tuccella et al., 2012).Localized negative biases in the model over the coast may be associated with the higher uncertainties in MODIS retrievals at coastlines.
Extreme AOD exhibits relatively large spatial scales of coherence in both the WRF-Chem simulations and MODIS L2 observations (Fig. 8).Consistent with prior analyses of L3 MODIS data (Sullivan et al., 2015), the largest scales of coherence are found in fall.In all seasons except for winter the probability of co-occurrence of extremes at the domain center and any other grid cell in the simulation domain is > 0.5 up to a distance Introduction

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Full of 300 km.The simulated mean seasonal scales of extreme coherence are comparable to L2 MODIS AOD (Fig. 8), despite the larger variability in the MODIS data due to the limited retrievals with simultaneous extreme AOD at the reference location and each other grid cell.Thus, consistent with prior research this analysis indicates the occurrence of extreme AOD occurs on large spatial scales and therefore may significantly impact regional climate.

Discussion and concluding remarks
Aerosol direct and indirect radiative forcing on the climate system are highly uncertain.A systematic assessment of the ability of global and regional models to reproduce aerosol optical properties in the contemporary climate is essential to increasing confidence in future projections.We contribute to this growing literature by presenting high resolution (12 km) simulations from WRF-Chem conducted over eastern North America during a year representative of average meteorological and aerosol conditions, and compare the results with daily MODIS and MISR observations, as well as with high frequency AERONET measurements of AOD and AE.Results from this study show: -After grid cells with any cloud presence are removed, the domain averaged mean AOD is 0.22.Simulated AOD is positively biased relative to observations, with MFB = 0.14 when comparing with MODIS-Aqua and 0.39 relative to AERONET (Figs. 2 and 4).This positive bias is consistent across the entire probability distribution at most AERONET stations (Fig. 6), and is also evident in comparison of modeled near-surface PM 2.5 mass relative to daily mean observations distributed at 1230 stations across the domain (Fig. 3).
-Model skill in reproducing the spatial fields of monthly mean AOD as measured by the spatial correlation and ratio of the spatial variability with MODIS is maximized during the summer months (r ∼ 0.5-0.7,and ratio of σ ∼ 0.8 to 1.2).During this season observed AOD is higher and more observations are available (Fig. 5).Introduction

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Full Lowest model-observations agreement is found in September and October and is at least partially attributable to a dry bias in WRF-Chem (Fig. S3).
-In part because of the difficulties in retrieving robust estimates of AE, few previous studies have evaluated model simulated AE values.We show that AE as simulated by WRF-Chem over eastern North America is negatively biased relative to MODIS (MFB = −0.10)and AERONET (MFB = −0.64).This bias indicates WRF-Chem simulates a larger fraction of coarse mode particles than is evident in the remote sensing observations (see Table 3 and 5).While some of the bias relative to MODIS may reflect high observational uncertainty, the bias relative to AERONET is consistent with prior research (Table 1) and is symptomatic of relatively poor model performance for this metric.Causes of the model error may include insufficiently detailed treatment of size distribution or inaccurate representation of aerosol composition and mixing state which affect the simulated size distribution and thus AE (Li et al., 2015;Curci et al., 2014).Further, weaknesses in the emission inventory (e.g.size resolution of primary emissions), as suggested by the systematic bias in simulated PM 2.5 concentrations relative to ground-based observations, and/or biases in the representation of meteorological conditions critical to determining aerosol nitrate concentrations may also affect model performance.Currently it is not possible to fully attribute the relative importance of these error sources.
-The majority of prior model evaluation exercises have tended to focus on the central tendency of the AOD probability distribution.However, the climate and health impacts of aerosols are maximized under high aerosol loadings.We demonstrate that WRF-Chem exhibits some skill in capturing the spatial patterns of extreme aerosol loading, especially during summer months.During this season, the Hit Rate for AOD > p75 reaches 70 %.Largest biases are found during winter months and near the coastlines where AOD from MODIS also exhibits largest retrieval uncertainty.Introduction

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Full  Full  Full  Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Grid cells with AOD exceeding that threshold were classified as exhibiting extreme values.The consistency in the spatial distribution of extreme values as simulated by WRF-Chem relative to MODIS are quantified using three skill statistics: the Accuracy, Hit Rate (HR) and Threat Score (TS) defined in Eqs.(5-7).In these equations, WE, ME, WN and MN correspond to occurrence of extreme conditions Discussion Paper | Discussion Paper | Discussion Paper |

2.
Evaluation of the scales of coherence of extreme AOD.For each day during the overpass time and hours of clear sky conditions, we determine if AOD simulated at our reference location (i.e. the center of the domain, in Southern Indiana) is equal or larger than the local p75 for that grid cell and season and then identify all grid cells in the domain that also satisfy the condition of AOD ≥ local p75.For each season, we thus compute the probability of extreme AOD co-occurrence at our reference site and any other grid cell as the frequency of co-occurrence divided by the number of extreme occurrences at the reference location.The spatial scales of extreme AOD are then estimated by binning the radial distance of each grid cell centroid from the domain center into 100 km distance classes.An analogous procedure is applied to L2 MODIS data to compare with simulations.
Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |Despite the encouraging performance of WRF-Chem both in terms of simulation efficiency and in reproducing AOD (mean and extreme values) and the partial skill in reproducing AE over eastern North America, further investigations are needed to properly quantify the value added by running high-resolution simulations by direct comparison with analogous runs at coarser resolution.Future simulations will also involve assessment of accuracy of different aerosol schemes (i.e.sectional vs. modal approaches) to represent the size distribution.The inclusion of a direct description of new particle formation processes within WRF-Chem may also improve estimates of ultrafine particle concentrations and thus of simulated aerosol optical properties.The Supplement related to this article is available online at doi:10.5194/acpd-15-27311-2015-supplement.Acknowledgements.This research was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute, and in part by the Indiana METACyt Initiative.The Indiana METACyt Initiative at IU is also supported in part by Lilly Endowment, Inc.Additional support was provided by the L'Oréal-UNESCO UK and Ireland Fellowship For Women In Science (to PC), the Natural Environmental Research Council (NERC) through the LICS project (ref.NE/K010794/1), the US NSF (grants # 1102309 and 1517365 to SCP) and a NASA Earth and Space Science Fellowship Program -Grant "14-EARTH14F-0207" (to RCS).The data used in this study were acquired as part of the NASA's Earth-Sun System Division, and archived and distributed by the MODIS Level 1 and Atmosphere Archive and Distribution System (LAADS), and the Giovanni online data system, developed and maintained by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC).We thank the PI investigators and their staff for establishing and maintaining the 22 AERONET sites used in this investigation.PM 2.5 surface concentrations from the United States Environmental Protection Agency were obtained from: http://www.epa.gov/airquality/airdata/ad_data_daily.html.Meteorological lateral boundary conditions from the North American Mesoscale model were obtained from the NOAA Operational Model Archive and Distribution System: ftp://nomads.ncdc.noaa.gov/NAM/analysis_only/. Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 2 .Figure 5 .Figure 6 .
Figure 2. Mean (a) AOD and (b) AE simulated by WRF-Chem during the year 2008.The mean values are computed after applying a cloud mask.Mean Fractional Bias (MFB) for (c) AOD and (d) AE for WRF-Chem relative to MODIS (Terra) (similar results are found for Aqua).The numbers in panels c-d are MFB for WRF-Chem vs. AERONET stations (red numbers indicate WRF-Chem vs. AERONET has a larger MFB than WRF-Chem vs. MODIS whereas black numbers indicate a lower bias in the comparison with AERONET).The inner black frame indicates the entire model domain, while as stated in the text model evaluation is only undertaken for longitudes east of 98 • W.
• W, 39 • N).The calendar year 2008 was selected because it is representative of average climate and aerosol conditions in the center of the model domain (near Indianapolis, IN).In 2008, mean T max , T min , precipitation, and wind speed as measured at the National Weather Service Automated Surface Observing Systems (NWS ASOS) station at Indianapolis International Airport are within ±0.25 standard deviations (σ) of the 2000-2013 seasonal means.Further, mean seasonal AOD from Level-3 MODIS retrievals is within ±0.2σ of 2000-2013 mean values.Additionally, choice of this year Introduction • W. All comparisons of modeled aerosol optical properties relative to MODIS observations (e.g.monthly mean values) only include grid cells for which at least 5 valid coincident observations are available during a given month after applying a cloud screen for overpass hours with cloud fraction larger than zero.It is worth noting that setting a threshold of 10 observations does not significantly affect the results.
For a uniform assessment, L2 MODIS and L3 MISR data have been interpolated from their native grids (and resolutions of 10 km and 0.5• × 0.5 • , respectively) to the WRF-Chem 12 km resolution grid by computing the mean of pixels with valid data within 0.1 • (∼ 20 km) from the model centroids.The choice of averaging over a slightly larger area than model resolution is dictated by the sparsity of valid MODIS retrievals.Where WRF-Chem output is compared with data from AERONET stations, a station is only included if there are at least 20 simultaneous estimates available.

Table 1 .
Synthesis of some recent prior studies comparing simulated aerosol optical properties from global or regional model simulations with remote sensing products.The first column summarizes the model used, the second the domain and the time period simulated and the third shows the model resolution and summarizes the description of the aerosol size distribution.Columns 4 to 9 summarize the evaluation statistics in terms of the overall correlation coefficient (R), bias (as described using the mean fractional error (MFE)) and root mean square error (RMSE) or mean absolute error (MAE) relative to satellite or AERONET observations as reported in the references shown in column 10.

Table 5 .
Contingency table showing the fraction of grid cells simultaneously identified as fine (WF/MF) or coarse (WC/MC) mode particles by WRF-Chem and MODIS, as well as cells with different classification (columns 4 and 5).Recall a threshold of AE = 1 is used to define fine (AE > 1) and coarse mode (AE < 1) dominance.Months in bold indicate the distribution of observed and simulated fine/coarse mode fractions are significantly different (p value < 0.01) according to the χ 2 test described in Sect.2.3.