An accurate characterization of the temporal distribution in primary emissions is essential for air quality modeling. This study evaluates the impact of replacing the default temporal profiles in the Copernicus Atmosphere Monitoring Service (CAMS) European air quality multi-model ensemble with an updated dataset (CAMS-REG-TEMPO). The sensitivity of 11 regional models and the ensemble to these changes is assessed by comparing modeled and observed monthly, weekly, and diurnal cycles of nitrogen dioxide (NO2), ozone (O3), coarse particulate matter (PM10), and fine particulate matter (PM2.5) across Europe. NO2 shows the greatest improvement, with weekly cycle correlations increasing up to +0.17 due to better road transport emissions representation. PM10 correlations improve in winter (up to +0.13 weekly and +0.07 diurnal) due to refined residential wood combustion emissions. PM2.5 correlations remain largely unchanged, except for diurnal cycles, which improve in winter (+0.18) but slightly degrade in spring and summer (−0.02). O3 is the least affected, as correlations were already high with default profiles (0.9–0.95). For some species and timescales (e.g., NO2 diurnal cycles), results vary across models, highlighting the complex interactions between emission timing and atmospheric processes. CAMS-REG-TEMPO has little effect on annual RMSE and bias, aside from slight improvements in high PM10 concentrations. Overall, the findings support implementing CAMS-REG-TEMPO in the operational CAMS multi-model ensemble.

Technical note: sensitivity of the CAMS regional air quality modelling system to anthropogenic emission temporal variability

Adani M.;D'Elia I.;Russo F.;
2025-01-01

Abstract

An accurate characterization of the temporal distribution in primary emissions is essential for air quality modeling. This study evaluates the impact of replacing the default temporal profiles in the Copernicus Atmosphere Monitoring Service (CAMS) European air quality multi-model ensemble with an updated dataset (CAMS-REG-TEMPO). The sensitivity of 11 regional models and the ensemble to these changes is assessed by comparing modeled and observed monthly, weekly, and diurnal cycles of nitrogen dioxide (NO2), ozone (O3), coarse particulate matter (PM10), and fine particulate matter (PM2.5) across Europe. NO2 shows the greatest improvement, with weekly cycle correlations increasing up to +0.17 due to better road transport emissions representation. PM10 correlations improve in winter (up to +0.13 weekly and +0.07 diurnal) due to refined residential wood combustion emissions. PM2.5 correlations remain largely unchanged, except for diurnal cycles, which improve in winter (+0.18) but slightly degrade in spring and summer (−0.02). O3 is the least affected, as correlations were already high with default profiles (0.9–0.95). For some species and timescales (e.g., NO2 diurnal cycles), results vary across models, highlighting the complex interactions between emission timing and atmospheric processes. CAMS-REG-TEMPO has little effect on annual RMSE and bias, aside from slight improvements in high PM10 concentrations. Overall, the findings support implementing CAMS-REG-TEMPO in the operational CAMS multi-model ensemble.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/87347
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