Air-sea interactions have large impacts on atmospheric circulation and planetary boundary layer (PBL) conditions (such as the PBL height (PBLH), surface temperature, and surface wind) through changing coastal convection and surface fluxes exchange. The changes in PBL conditions will affect the concentrations, distributions, and lifetimes of atmospheric pollutants. While the impacts of air-sea interactions on atmospheric dynamics and ocean circulation have been assessed [1
], their impacts on meteorology and atmospheric pollutants have not been extensively studied. He et al. [5
] compared model simulations over the southern U.S. using the Weather Research and Forecasting model with Chemistry (WRF/Chem) without air-sea interactions, WRF/Chem with a 1-D ocean mixed layer model (WRF/Chem-OML), and WRF/Chem coupled with a 3-D Regional Ocean Modeling System (WRF/Chem-ROMS). They reported that the differences in the simulated surface concentrations of O3
between WRF/Chem-ROMS and WRF/Chem can be as large as 17.3 ppb and 7.9 μg m−3
, respectively, because WRF/Chem-ROMS explicitly simulates sea-surface temperature (SST), whereas WRF/Chem prescribes it. They also found that WRF/Chem-ROMS improves the predictions of most cloud and radiative variables and surface concentrations of a number of chemical species.
In addition to the model representations of major processes, the performance of a model is affected by the accuracy of the model inputs, such as emissions and initial and boundary conditions. The importance of chemical initial and boundary conditions (ICONs and BCONs) on air quality has been extensively studied in the Northern Hemisphere, but has seldom been reported for model applications in the Southern Hemisphere. These studies reported the impacts of ICONs and BCONs for several species including O3
], CO [12
], and particulate matter with diameters less than or equal to 2.5 and 10 μm (PM2.5
, respectively) [10
], and their impacts on air quality. These studies suggested the use of spatial and time-varying BCONs, which can improve the model performance, compared to static or idealized BCONs [6
]. They also reported sensitivity of the model predictions to chemical ICONs and BCONs derived from different global models. For example, Yahya et al. [11
] compared 2006 and 2010 model predictions using WRF/Chem v3.4.1 with ICONs and BCONs from two global models, CESM and MACC, and found that the simulations with the CESM ICONs and BCONs improved the performance for O3
mixing ratios in both years, PM2.5
in 2010, and sulfate in 2006. Zhang et al. [12
] showed substantial improvement for both spatial distribution and domain-mean performance statistics over the southeastern U.S. using satellite-constrained BCONs for O3
and CO, reducing normalized mean biases (NMBs) of column O3
and CO from 44.8% to −0.01% and −40.6% to −10.6%, respectively.
To understand the ambient air quality in Australia, in particular the Sydney area, evaluate the current models’ skill in reproducing atmospheric observations, and study the importance of air-sea interactions, the North Carolina State University’s version of WRF/Chem v3.7.1 (WRF/Chem-NCSU) and the coupled North Carolina State University’s WRF/Chem v3.7.1 with ROMS (WRF/Chem-ROMS) have been applied over quadruple-nested domains at grid resolutions of 81, 27, 9, and 3 km over Australia (d01), an area in southeastern Australia (d02), an area in New South Wales (d03), and in the Greater Sydney area (d04), respectively. The model simulations were performed during three field campaigns conducted in New South Wales (NSW), including the Sydney Particle Study (SPS) Stages 1 and 2 in western Sydney, during summer 2011 (5 February to 7 March) and autumn 2012 (16 April to 14 May), respectively [15
], and the Measurements of Urban, Marine, and Biogenic Air (MUMBA) campaign in Wollongong during summer 2013 (21 December–15 February 2013) [17
Part I presents a comprehensive evaluation of WRF/Chem-ROMS using surface data from the three field campaigns and satellite data. In this part II paper, the results from WRF/Chem are evaluated and compared with those of WRF/Chem-ROMS to assess the impact of air-sea interaction representation on meteorological and chemical predictions. In addition, the impact of chemical BCONs is assessed.
2. Model Description and Adjustment of Boundary Conditions
The two models used in this study are WRF/Chem-NCSU and WRF/Chem-ROMS. The model configurations and evaluation protocols are described in detail in the Part I paper. A more detailed description of WRF/Chem-NCSU can be found in Wang et al. [18
] and Yahya et al. [19
] and that of WRF/Chem-ROMS can be found in He et al. [5
]. While WRF/Chem-NCSU is an online-coupled meteorology and chemistry model, WRF/Chem-ROMS is an online-coupled meteorology, chemistry, and ocean model. As indicated in Part I, the WRF/Chem-ROMS simulation uses the same physics and chemistry options as WRF/Chem with only one difference, WRF/Chem-ROMS explicitly represents air-sea interactions and simulates SST, whereas WRF/Chem-NCSU does not simulate air-sea interactions and it prescribes SST based on NCEP high-res SST analysis (RTG_SST) data [20
]. The RTG_SST was generated based on satellite retrieval and created daily in near-real time (https://polar.ncep.noaa.gov/sst/rtg_high_res/description.shtml
). This dataset has several weaknesses. For example, the resolution of data is relatively coarse with 0.5° × 0.5°, the accuracy of the data is directly affected by the satellite data used for the SST retrieval and, thus, contains relatively high uncertainties, compared to other reanalysis data, and the data may contain higher biases when the precipitating clouds are presented. Since the WRF/Chem simulation is initialized once at the beginning of the simulation, the prescribed SST remains constant throughout the simulation for each field campaign. For WRF/Chem-ROMS, the SST is simulated real-time and updated hourly.
In the initial applications of the two models, BCONs of CO, NO2
, HCHO, O3
, and PM2.5
are calculated based on global model predictions from the Community Earth System Model (CESM-NCSU) v1.2.2. To improve the accuracy in the chemical boundary conditions, these values are adjusted using satellite-constrained values, which directly affect predictions over d01 at 81 km and indirectly affect predictions over the other domains (d02–d04). The satellite retrievals used for adjustment are the column abundance of carbon monoxide (CO) from the Measurements of Pollution in the Troposphere (MOPITT), columns of nitrogen dioxide (NO2
) and formaldehyde (HCHO) from the Global Ozone Monitoring Experiment (GOME), the tropospheric ozone residual (TOR) from the Ozone Monitoring Experiment (OMI), and the aerosol optical depth (AOD) (a proxy for aerosol column concentrations) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly average values from both satellite retrievals and CESM-NCSU, their ratios, as well as the average values along each side of the domain (by averaging a band containing 5 row or column of satellite retrievals depending on different side of boundaries) are first calculated. The four adjustment factors for each side are then calculated using these retrieval-to-model ratios and applied for all vertical profiles of the BCONs for these species. The adjustment factors for PM2.5
are applied for the BCONs of all the major PM species. No adjustment is made for boundaries with poor quality of satellite data. Table S1 in the supplementary material
summarizes the adjustment factors used for model simulations of SPS1, SPS2, and MUMBA. Using satellite retrievals as constraints, the values of BCONs are increased for CO, O3
, and HCHO and decreased for NO2
for all three field campaign periods. Those for PM2.5
are increased for SPS2 and MUMBA, but decreased for SPS2.
4. Impact of Chemical Boundary Conditions
summarizes the normalized mean biases (NMBs) of column and surface chemical predictions from WRF/Chem simulations without and with adjusted BCONs at 81 km (d01), 27 km (d02), 9 km (d03), and 3 km (d04) over the 3 km domain (d04). Figure 12
and Figure S6
compare satellite observed column mass abundances of gases and AOD with those simulated by WRF/Chem-NCSU without and with adjustment of BCONs over Greater Sydney (d04) during SPS1 and SPS2, respectively. The predictions from WRF/Chem-ROMS without and with adjustment of BCONs are similar to those from WRF/Chem-NCSU, thus not shown here. Among all the column variables, the spatial distributions and magnitudes of column CO with adjusted BCONs are obviously improved over all domains. The NMBs in d01 reduce largely from −31.1% to −22.0% for SPS1, from −25.4% to −7.3% for SPS2, and from −26.4% to −17.5% for MUMBA. Similar improvements are found for model predictions at all finer grid resolutions. For example, the NMBs of column CO predictions in d04 reduce largely from −30.5% to −21.3% for SPS1, from −24.9% to −6.8% for SPS2, and from −25.9% to −17.1% for MUMBA. For column NO2
, NMBs with adjusted BCONs are slightly worse at 81, 27, and 9 km, but slightly better at 3 km for SPS1, slightly better at all grid resolutions for SPS2, and slightly worse at 81km, but slightly better at other finer grid resolutions for MUMBA. For example, the NMBs in d04 reduce slightly from −24.1% to −23.2% for SPS1, from −23.3% to−21.2% for SPS2, and from −14.3% to −12.4% for MUMBA. The simulated spatial distributions for column NO2
without and with adjusted BCONs are similar, both simulations capture well the hot spots but tend to overpredict the values of these hot spots, but underpredict column NO2
in other regions. For column HCHO, NMBs are slightly better at all grid resolutions for SPS1 and MUMBA and slightly worse at all grid resolutions for SPS2. The spatial distributions for column HCHO without and with adjusted BCONs are very similar for all field campaigns, with moderate underpredictions for SPS1 and SPS2 and slight underpredictions for MUMBA. For column O3
, NMBs with adjusted BCONs are slightly better for SPS2 but worse for SPS1 and MUMBA. The spatial distributions of simulated column O3
without and with adjusted BCONs do not agree with the satellite observations, with slight overpredictions for SPS2 but moderate overpredictions for SPS1 and MUMBA. The O3
lateral boundary condition is adjusted using a fixed factor throughout all altitudes for each boundary side. More accurate adjustments by considering the shape of O3
profiles (i.e., altitude weighted adjustment factors) need to be considered in the future work. For AOD, NMBs are better at all grid resolutions for SPS1 and at 81 km and 27 km for SPS2, but slightly worse at all grid resolutions for MUMBA.
The adjusted chemical BCONs can affect surface concentration predictions. For example, NMBs of surface CO and NO predictions over d04 reduce from −46.3% to −44.5% and 17.8% to 6.5% for SPS1, from −33.0% to −30.2%, and 4.9% to −0.1% for SPS2, and from −20.0% to −18.2% and 129.4% to 87.9% for MUMBA, respectively. NMBs of surface O3
predictions at 3 km resolution over d04 reduce from −21.3% to −5.8% for SPS1, from −9.2% to 4.9% for SPS2, and from −37.8% to −15.6% for MUMBA. NMBs of surface PM2.5
predictions at 3 km over d04 are slightly better for SPS1 and MUMBA and those of surface PM10
predictions are slightly better over d04 at all grid resolutions. Figure S7
compares observed temporal variations of surface CO mixing ratios with two sets of surface CO predictions from WRF/Chem-NCSU at 3 km resolution at selected sites during SPS1; one using CO BCONs derived from CESM-NCSU simulations and one using CO BCONs constrained by satellite data. Slight improvement in surface CO predictions is found at all sites.
compares observed temporal variations of surface O3
mixing ratios with two sets of O3
predictions from WRF/Chem-NCSU at 3 km at selected sites during SPS1 without and with adjusted O3
BCONs. Using satellite-constrained chemical BCONs for O3
appreciably improves the performance of O3
during most days at all sites. Compared to several studies on the importance of chemical BCONs over North America [9
], the magnitude of relative changes in surface O3
concentrations due to changes in chemical BCONs of O3
is larger in Australia. For example, Yahya et al. [11
] reported changes in the range of NMBs for maximum 8 h mixing ratios from −11.8% to −5.6% in 2006 and −13.0% to −11.1% in 2010 at rural sites and from −2.8% to −1.7% in 2006 and −1.1% to −7.0% in 2010 at urban and suburban sites in the continental U.S., when changing the chemical BCONs based on results from one global model to another. These relative changes are much smaller than those in this work (e.g., adjusted BCONs reduce NMBs of surface O3
concentrations from −21.3% to −5.8% for SPS1 and from −37.8% to −15.6% for MUMBA) because of lower surface O3
levels in Australia than those over North America, indicating a more important role of chemical BCONs in the Southern Hemisphere compared to the Northern Hemisphere.
As described in the Part I paper, the only difference between WRF/Chem and WRF/Chem-ROMS lies in the fact that air-sea interactions are not treated in WRF/Chem, but are explicitly represented in WRF/Chem-ROMS through a dynamic atmosphere-ocean coupling. As a result, SST is simulated in WRF/Chem-ROMS but prescribed based on RTG_SST analysis data in WRF/Chem. In this Part II paper, the results from WRF/Chem are evaluated using surface observations from the three field campaigns, satellite retrievals, and combined satellite and reanalysis data and compared with those of WRF/Chem-ROMS to assess the impact of air-sea interaction representation on meteorological and chemical predictions. A second objective of this Part II paper is to assess the impact of chemical BCONs used on overall model predictions.
At spatial grid resolutions of 3, 9, and 27 km, compared to WRF/Chem, the explicit air-sea interactions in WRF/Chem-ROMS lead to substantial improvements in terms of performance statistics and spatial distributions in the model’s predictability of sea-surface temperature, latent heat fluxes, and sensible heat fluxes over the ocean. For example, the MBs of SST are within ±1 °C for WRF/Chem-ROMS but can be up to ±3.3 °C for WRF/Chem during all three field campaigns. WRF/Chem-ROMS assembles the spatial distributions and gradients of SST, LHF, and SHF much closer to those from OAFlux. The use of finer grid resolutions (3 or 9 km) effectively reduces the biases in SST, LHF, and SHF during SPS1 and SPS2 by WRF/Chem-ROMS, whereas it further increases these biases for WRF/Chem during all field campaigns.
The large differences in SST, LHF, and SHF between the two models lead to different radiative, cloud, and meteorological predictions. For example, over the Greater Sydney area, the percentage differences between WRF/Chem-ROMS and WRF/Chem are −7.3% to 18% for net shortwave radiation, −4.1% to 2.6% for downward longwave radiation, −31.7% to 13.1% for T2, −15.2% to 30.8% for WS10, −37.3% to 73.1% for PBLH, and as large as ±20% for shortwave cloud forcing and ±50% for precipitation. While WRF/Chem-ROMS gives either similar or slightly worse performance for most radiative and cloud variables, it generally gives better performance in terms of performance statistics and temporal variations for meteorological predictions such as T2, RH2, WS10, WD10, and precipitation, with a few exceptions. For example, during SPS2, the WRF/Chem-ROMS simulation at 3 km reduces NMBs of precipitation from −46.7% to −31.3%, from −48.3% to −33.4%, and from 21.3% to −6.6% against data from OBS, MSWEP, and GPCP, respectively. WRF/Chem-ROMS also better captured heavy precipitation events on 18 April 2012 at Badgery’s Creek, Bellambi, Sydney Airport, and Williamtown RAAF during SPS2 and 29January 2013 at Sydney Airport and 2 February 2013 at all sites during MUMBA.
The differences in radiative, cloud, and meteorological predictions, in turn, cause different chemical predictions. For example, compared to WRF/Chem results, the decreased PBLH from WRF/Chem-ROMS may explain in part the increased surface CO, SO2, and NO2 mixing ratios in most regions during SPS2. The increased T2 for SPS1 and MUMBA from WRF/Chem-ROMS lead to higher isoprene and, thus, higher HCHO mixing ratios over land areas. The opposite occurs for SPS2. The changes in WS10 simulated by WRF/Chem-ROMS can affect the emissions of sea-salt particles, which in turn affect PM10 concentrations. As a result, the percentage differences in surface concentrations are mostly in the range of ±10% for CO, OH, and O3, ±25% for HCHO, ±30% for NO2, ±35% for H2O2, ±50% for SO2, ±60% for isoprene and terpenes, ±15% for PM2.5, and ±12% for PM10. Compared to WRF/Chem, WRF/Chem-ROMS can increase the concentrations of O3, PM2.5, and PM10 by up to 3.1 ppb, 3.9 μg m−3, and 10.1 μg m−3, and decrease them as large as 3.4 ppb, 2.3 μg m−3, and 8.2 µg m−3, with domain-mean changes of −1.3 to 0.7 ppb (−6.1% to 4.0%), 0.03–0.2 µg m−3 (1.2–7.3%), and 0.02–0.5 μg m−3 (0.6-7.4%), respectively, for O3, PM2.5, and PM10. The WRF/Chem-ROMS simulations at 3 km resolution slightly improves the statistical performance of surface concentrations of CO, NO, PM2.5, PM10, column NO2 and HCHO for SPS1, CO, O3, PM10, column NO2, and TOR for SPS2, and NO, NO2, O3, PM2.5, PM10, and column CO, NO2 and HCHO for MUMBA.
The BCONs of CO, NO2, HCHO, O3, and PM2.5 are constrained based on satellite retrievals of column CO, NO2, HCHO, O3, and AOD, respectively. Compared to WRF/Chem simulations without adjusted BCONs, WRF/Chem simulations with satellite-constrained BCONs improve the spatial distributions and magnitudes of column CO at 81 km over d01, which leads to improvement at 27, 9, and 3 km in d04. For example, the NMBs of column CO predictions in d04 reduce largely from −30.5% to −21.3% for SPS1, from −24.9% to −6.8% for SPS2, and from −25.9% to −17.1% for MUMBA. The use of satellite-constrained BCONs also slightly improves the performance of column NO2 for SPS1 and SPS2, column HCHO for SPS1 and MUMBA, and TOR for SPS2 at 3 km resolution over d04. The adjusted BCONs reduce the NMBs of surface CO, NO, and O3 predictions at 3 km over d04 from −46.3% to −44.5%, from 17.8% to 6.5%, and from −21.3% to −5.8% for SPS1, from −33.0% to −30.2%, from 4.9% to −0.1%, and from −9.2% to 4.9% for SPS2, and from −20.0% to −18.2%, from 129.4% to 87.9%, and from −37.8% to −15.6% for MUMBA, respectively. They reduce slightly the NMBs of surface PM2.5 predictions at 3 km resolution over d04 for SPS1 and MUMBA and those of surface PM10 predictions at all grid resolutions for all field campaigns. Compared to results from past studies on the impact of chemical BCONs on surface O3 concentrations over North America, the magnitude of relative changes in surface O3 concentrations due to changes in chemical BCONs of O3 reported in this work is larger because of lower surface O3 levels in Australia than those in North America. This indicates a more important role of chemical BCONs in the Southern Hemisphere compared to the Northern Hemisphere and, thus, a crucial need for more realistic chemical BCONs of O3 in the relatively clean Southern Hemisphere.