Evaluating High-Resolution Forecasts of Atmospheric CO and CO<sub>2</sub> from a Global Prediction System during KORUS-AQ Field Campaign

Abstract

Abstract. Accurate and consistent monitoring of anthropogenic combustion is imperative because of its significant health and environmental impacts, especially at city-to-regional scale. Here, we assess the performance of the Copernicus Atmosphere Monitoring Service (CAMS) global prediction system using measurements from aircraft, ground sites, and ships during the Korea United States Air Quality (KORUS-AQ) field study in May to June 2016. Our evaluation focuses on CAMS CO and CO 2 analyses plus two higher resolution forecasts (16-km and 9-km horizontal resolution), to assess their 5 capability in predicting combustion signatures over East Asia. Our results show a slight overestimation of CAMS CO 2 with a mean bias against airborne CO 2 measurements of 2.2, 0.7, and 0.3 ppmv for 16-km and 9-km CO 2 forecasts, and analyses, respectively. The positive CO 2 mean bias in the16-km forecast appears to be consistent across the vertical profile of the measurements. In contrast, we find a moderate underestimation of CAMS CO with an overall bias against airborne CO measurements of -19.2 (16-km), -16.7 (9-km), and -20.7 ppbv (analysis). This negative CO mean bias is mostly seen below 10 750 hPa for all three forecast/analysis configurations. Despite these biases, CAMS show a remarkable agreement with observed enhancement ratios of CO with CO 2 over Seoul metropolitan and over the West Sea, where East Asian outflows were sampled during the study period. More efficient combustion is observed over Seoul (∆CO/∆CO % = 9 ppbv/ppmv) compared to the West Sea (∆CO/∆CO % = 28 ppbv/ppmv). This 'combustion signature contrast' is consistent with previous studies in these two regions. CAMS captured this difference in enhancement ratios (Seoul: 8-12 ppbv/ppmv, West Sea: ~30 15 ppbv/ppmv) regardless of forecast/analysis configurations. The correlation of CAMS CO bias with CO 2 bias is relatively high over these two regions (Seoul: 0.64-0.90, West Sea: ~0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO 2 variability. Along with measurements at ground sites over the Korean peninsula, CAMS produces too high CO and CO 2 concentrations at the surface with steeper vertical gradients (~0.4 ppmv/hPa for CO 2 20 and 3.5 ppbv/hPa for CO) in the morning samples than observed (~0.25 ppmv/hPa for CO 2 and 1.7 ppbv/hPa for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9-km vs 16-km) generally produce better forecasts.

Using Short‐Term CO/CO2 Ratios to Assess Air Mass Differences Over the Korean Peninsula During KORUS‐AQ

“…Figure 4b quantifies the percentages of correlated slopes that fall within each ΔCO/ΔCO 2 ratio range. The results from these three regions agree with the observed and modeled ΔCO/ΔCO 2 values in Tang et al (2018), which assessed the Copernicus Atmosphere Monitoring Service CO and CO 2 forecasts. Figure 5 summarizes the dynamic ranges for the slopes shown in Figure 4, showing the range of the calculated 60-s ΔCO and ΔCO 2 for the correlated slopes used in the analysis, separated by analysis region.…”

Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation

“…The KORUS‐AQ measurements over the Yellow Sea were designed to observe strong (sharp) transport of pollution plumes from China, whereas even at the improved T106, the horizontal model resolution is still considered too coarse to simulate the plume structure. Tang et al () noted that during the 25 May 2016 flight, even forecasts using a grid spacing of 9 × 9 km were not able to capture the transport of enhanced CO and CO 2 over the Yellow Sea, although the impact of using high resolution models for simulating the high ozone plume is not clear. Based on model calculations using the optimized emission (cf., section ), we confirmed that rapid transport of polluted air from China through NO x emissions resulted in large enhancements of lower tropospheric ozone (30–50 ppbv) over the Yellow Sea during the period (Figure ).…”

“…We used DC‐8 aircraft measurements (23 flights) and ozonesonde measurements over Taehwa (42 profiles, located in a forest area near Seoul) and Olympic Park (20 profiles, located in an urban area of Seoul). Some of the DC‐8 flight track information is provided by Tang et al (). Sixty‐second merge R4 data of various species measurements on board DC‐8 were used: ozone, NO 2 , and NO obtained using four‐channel chemiluminescence instrument (Weinheimer et al, ); NO 2 using thermal‐dissociation laser‐induced fluorescence (Thornton et al, ); OH and HO 2 concentrations using laser‐induced fluorescence; OH reactivity with discharge flow (Brune et al, ); CO using differential absorption carbon monoxide measurements (Warner et al, ); CH 2 O using the compact atmospheric multispecies spectrometer (Weibring et al, ); PAN and SO 2 using the chemical ionization mass spectrometer (Huey, ); and HNO 3 using single mass analyzer chemical ionization mass spectrometer (Crounse et al, ).…”

Ceilometer Monitoring of Boundary-Layer Height and Its Application in Evaluating the Dilution Effect on Air Pollution

“…For further verification, the algorithms including the adjusted parameters were tested against Z ΔT values retrieved from radiosonde observations conducted during the Korea-United States Air Quality (KORUS-AQ) field campaign (Tang et al 2018). This was an intensive campaign in which four radiosondes were launched during the daytime for four days of May 2016 at the Olympic Park in Seoul.…”

“…To investigate the relationship between the evolution of the UBL and air pollution, we investigate the application of inverse regressions of first order between the retrieved values of Z ML and air-quality data from the local monitoring network. Even though Korea has implemented stringent control measures aimed at reducing the domestic emission of pollutants, the rapid industrialization and urbanization of East Asia have hampered to some extent such measures, e.g., Lee et al (2011Lee et al ( , 2013, Vellingiri et al 2015, Chambers et al (2017) and Tang et al (2018). To investigate the impact of regional air pollution over Seoul we use data from a background air-quality monitoring station to determine the height at which local pollution reaches its regional levels for the cases of sulfur dioxide (SO 2 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter of diameter smaller than 10 µm (PM 10 ).…”