Jan Karlický

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied at high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8 h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx at horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction at high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.

While overall the global warming with the causes and global processes connected to well-mixed CO2, and its impacts on global to continental scales are well understood with a high level of confidence, there are knowledge gaps concerning the impact of many other non-CO2 radiative forcers leading to low confidence in the conclusions. This relates mainly to specific anthropogenic and natural precursor emissions of short-lived GHGs and aerosols and their precursors. These gaps and uncertainties also exist in their subsequent effects on atmospheric chemistry and climate, through direct emissions dependent on changes in e.g., agriculture production and technologies based on scenarios for future development as well as feedbacks of global warming on emissions, e.g., permafrost thaw. The main goal of EC HE project FOCI, is to assess impacts of key radiative forcers abd the processes of their impact on the climate system, to find and test an efficient implementation of these processes into global ESMs and into RCMs coupled with CTMs, and finally to use the tools developed to investigate mitigation and/or adaptation policies incorporated in selected scenarios of future development targeted at Europe and other regions of the world. We are developing new regionally tuned scenarios based on improved emissions to assess the effects of non-CO2 forcers. Overall introduction to coupled RCM-CTM modelling experiment strategies and evaluation simulations will be presented in addition to the contemporary status of the project. Historical simulations results are validated against reanalyses data and the assessment of impact of chemistry involvement is shown. We will show the results for regional and local conditions in high resolution for City of Prague.

Fine particulate matter (PM_2.5) pollution remains a critical health issue in Europe. While numerous studies have quantified the spatio-sectoral sources of urban PM, the temporal origin has received minimum attention. This study addresses this gap by developing a Temporal Source Apportionment approach within the CAMx chemical transport model to quantify the long-term contributions of emissions from the preceding 14 d to PM concentrations, focusing on the 2010–2019 period and Central Europe. The novelty of the study lies in a long term and continuous adoption of TSA unlike previous attempts to quantify the age distribution of polluting aerosol that focused on selected air pollution periods. The results show that current-day emissions dominate winter PM_2.5, contributing 30 %–60 % on average, while day-1 emissions add further 20 %–30 %. Contributions decrease with emission age, falling below 5 % after 3 d and becoming negligible beyond 7 d. Secondary inorganic aerosols and primary organic aerosols exhibit similar patterns, although for winter nitrate levels, the highest contribution comes from day-1 emissions, reflecting the time needed for chemical formation. Summer contributions are smaller due to enhanced mixing and faster removal, whereas biogenic emissions also contribute largely, giving anthropogenic emissions a smaller role. Importantly, while the average contribution of older emissions is low, occasional episodes show substantial impacts: day-4 emissions can contribute up to 10 %, and even week-old emissions can add 2 % in winter. These findings emphasize that adverse air quality episodes are influenced not only by same-day emissions but also by pollution accumulated from previous days resulting from past emissions. Effective mitigation policies on PM pollution must therefore consider reducing emissions several days in advance of predicted pollution episodes, rather than relying solely on same-day interventions.

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied in high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8-h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx in horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction in high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied in high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8-h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx in horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction in high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied in high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8-h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx in horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction in high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied in high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8-h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx in horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction in high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.

Urban environments are hotspots of anthropogenic emissions, impact the warming rate over cities, and induce changes in several relevant meteorological variables such as wind speed, humidity and temperature, which in turn affect air quality and human health. Therefore, it is important to identify how urban parameterizations impact the regional-to-local scale processes in regional climate model simulations. To evaluate these impacts, we use the Weather Research and Forecasting (WRF) model with different urban schemes. The simulated period covers a timespan of 10 year and has an especial focus on the city of Prague (Czech Republic). To evaluate the results obtained, data from observations from the Czech Hydrometeorological Institute were used. Changes in temperature and specific humidity are mostly sensitive to the urban scheme selected, while changes in precipitation and cloud cover are less sensitive to the urban parameterization but more sensitive to the parameterization of convection and microphysics.

Chemistry transport models (CTMs) consistently exhibit systematic errors in ozone concentrations, which can be partly compensated by bias correction. There are several bias correction strategies suitable for using station data, but they are likely to introduce statistical artifacts when applied in high resolution. We propose a new bias correction strategy based on parametric interpolation of quantile biases (PIQB) suitable for high resolution simulations, which is designed to avoid such artifacts. In this study, we evaluate and compare the performance of our strategy with other older strategies with a focus on ambient maximum daily 8-h average ozone concentrations (MDA8). Our experimental setup consisted of two simulations from the CTMs WRF-Chem and CAMx in horizontal resolution of 9 km within the time period of 2007–2016 and 165 ground-based stations in central Europe. Our results show that each strategy brings the simulated MDA8 closer to observations, but PIQB performs the best in terms of mitigating systematic errors while retaining the modeled fine resolution structure of spatial variability. We conclude that out of the considered strategies, PIQB is the most suitable one for bias correction in high resolution, suggesting its possible applications for correcting climate projections of ozone MDA8.