Temporal variability of tropospheric ozone and ozone profiles in the Korean Peninsula during the East Asian summer monsoon: insights from multiple measurements and reanalysis datasets
Abstract:Abstract. We investigate the temporal variations of ground-level ozone and
balloon-based ozone profiles at Pohang (36.02∘ N, 129.23∘ E) in
the Korean Peninsula. Satellite measurements and chemical reanalysis products
are also intercompared to address their capability of providing consistent
information on the temporal and vertical variability of atmospheric ozone.
Sub-seasonal variations of the summertime lower-tropospheric ozone exhibit a
bimodal pattern related to atmospheric weather patterns modulated by th… Show more
“…In the UTLS, ECHAM-CTL shows underestimation while ERA5, CAMS, and MERRA2 show overestimation compared to ozonesondes. The ozone profiles at Pohang (36.02°N, 129.23°E) in the Korean Peninsula, in comparison with reanalysis products (MERRA2 and CAMS), also show largely overestimation (by 150 ppb) in the troposphere and stratosphere 40 . Figure 3 The peak value of ozone mixing ratio from PDF curve at different pressure levels ozonesonde (black), ERA5 (red), CAMS (blue), MERRA2 (cyan), and ECHAM–CTL (green).…”
Section: Comparison Of Ozonesondes With Reanalysis and Echam6-hammoz ...mentioning
The variability and trend of ozone (O3) in the Upper troposphere and Lower Stratosphere (UTLS) over the Asian region needs to be accurately quantified. Ozone in the UTLS radiatively heats this region and cools the upper parts of the stratosphere. This results in an impact on relative humidity, static stability in the UTLS region and tropical tropopause temperature. A major challenge for understanding ozone chemistry in the UTLS is sparse observations and thus the representation of precursor gases in model emission inventories. Here, we evaluate ozonesonde measurements during August 2016 at Nainital, in the Himalayas, against ozone from multiple reanalyses and the ECHAM6-HAMMOZ model. We find that compared to measurements both reanalyses and ECHAM6-HAMMOZ control simulation overestimate ozone mixing ratios in the troposphere (20 ppb) and in the UTLS (55 ppb). We performed sensitivity simulations using the ECHAM6-HAMMOZ model for a 50% reduction in the emission of (1) NOx and (2) VOCs. The model simulations with NOX reduction agree better with the ozonesonde observations in the lower troposphere and in the UTLS. Thus, neither reanalyses nor ECHAM6-HAMMOZ results can reproduce observed O3 over the South Asian region. For a better representation of O3 in the ECHAM6-HAMMOZ model, NOX emission should be reduced by 50% in the emission inventory. A larger number of observations of ozone and precursor gases over the South Asian region would improve the assessment of ozone chemistry in models.
“…In the UTLS, ECHAM-CTL shows underestimation while ERA5, CAMS, and MERRA2 show overestimation compared to ozonesondes. The ozone profiles at Pohang (36.02°N, 129.23°E) in the Korean Peninsula, in comparison with reanalysis products (MERRA2 and CAMS), also show largely overestimation (by 150 ppb) in the troposphere and stratosphere 40 . Figure 3 The peak value of ozone mixing ratio from PDF curve at different pressure levels ozonesonde (black), ERA5 (red), CAMS (blue), MERRA2 (cyan), and ECHAM–CTL (green).…”
Section: Comparison Of Ozonesondes With Reanalysis and Echam6-hammoz ...mentioning
The variability and trend of ozone (O3) in the Upper troposphere and Lower Stratosphere (UTLS) over the Asian region needs to be accurately quantified. Ozone in the UTLS radiatively heats this region and cools the upper parts of the stratosphere. This results in an impact on relative humidity, static stability in the UTLS region and tropical tropopause temperature. A major challenge for understanding ozone chemistry in the UTLS is sparse observations and thus the representation of precursor gases in model emission inventories. Here, we evaluate ozonesonde measurements during August 2016 at Nainital, in the Himalayas, against ozone from multiple reanalyses and the ECHAM6-HAMMOZ model. We find that compared to measurements both reanalyses and ECHAM6-HAMMOZ control simulation overestimate ozone mixing ratios in the troposphere (20 ppb) and in the UTLS (55 ppb). We performed sensitivity simulations using the ECHAM6-HAMMOZ model for a 50% reduction in the emission of (1) NOx and (2) VOCs. The model simulations with NOX reduction agree better with the ozonesonde observations in the lower troposphere and in the UTLS. Thus, neither reanalyses nor ECHAM6-HAMMOZ results can reproduce observed O3 over the South Asian region. For a better representation of O3 in the ECHAM6-HAMMOZ model, NOX emission should be reduced by 50% in the emission inventory. A larger number of observations of ozone and precursor gases over the South Asian region would improve the assessment of ozone chemistry in models.
“…In the UTLS, ECHAM-CTL shows underestimation while ERA5, CAMS, and MERRA2 show overestimation compared to ozonesondes. The ozone pro les at Pohang (36.02 o N, 129.23 o E) in the Korean Peninsula, in comparison with reanalysis products (MERRA2 and CAMS), also show largely overestimation (by 150 ppb) in the troposphere and stratosphere 34 .…”
Section: Probability Density Function (Pdf) Analysismentioning
confidence: 93%
“…Further, we show a comparison of all data sets for the special case on 15th August 2016, when there was no ATAL observed over Nainital in the UTLS region 34,35 . The ozone pro le on this day shows a sharp, deep low ozone concentration near 140 to 100 hPa (363-380 K).…”
Section: Comparison Of Ozone Pro Les On a Special Case Day 15 August ...mentioning
The variability and trend of ozone (O3) in the Upper troposphere and Lower Stratosphere (UTLS) over the Asian region needs to be accurately quantified. A major challenge for understanding ozone chemistry is sparse observations in the region and thus the representation of precursor gases in model emission inventories. Here, we evaluate ozonesonde measurements during August 2016 at Nainital, in the Himalayas, against ozone from multiple reanalyses and the ECHAM6-HAMMOZ model. We find that compared to measurements both reanalyses and ECHAM6-HAMMOZ control simulation overestimate ozone mixing ratios in the troposphere (20 ppb) and in the UTLS (55 ppb). We performed sensitivity simulations using the ECHAM6-HAMMOZ model for a 50% reduction in the emission of (1) NOx and (2) VOCs. The model simulations with NOX reduction agree better with the ozonesonde observations in the lower troposphere and in the UTLS. Thus, neither reanalysis nor ECHAM6-HAMMOZ can reproduce observed O3 over the South Asian region. For a better representation of O3 in the ECHAM6-HAMMOZ model, NOX emission should be reduced by 50% in the emission inventory. A larger number of observations over the South Asian region would improve the assessment of ozone chemistry in models.
“…It has a swath width of 2600 km and provides a spatial resolution of 13 km × 24 km. OMI can complete a global scan in just one day, measuring column concentrations and profiles of O 3 , NO 2 , SO 2 , as well as data on aerosols, clouds, surface ultraviolet radiation, and various other parameters 18 , 44 . Previous studies have shown that the OMI ozone column concentrations and profile data exhibit a reasonable consistency with lower- to mid-troposphere ozone across the world 17 , 45 .…”
Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015–2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.
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