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Statistical downscaling models are used to estimate historical and future ozone concentrations in Europe for the summer months from April to September. The basis is formed by over 700 measuring stations from the hourly Air Quality eReporting ozone pollution data from the European Environment Agency. Daily maximum 8-hr running means (MDA8) as well as daily maximum 1-hr values (MDA1) of ozone are the target variables. Meteorological (ERA5) as well as ozone (CAMS) reanalysis data serve as predictors in the perfect prognosis (PP) approaches. A station-specific, individualized predictor screening guarantees site-specific optimums. The predictor selection is performed using regularization with varying shrinkage. Multiple Linear Regression (MLR) is used to model the relationship between all selected predictors and MDA8/ MDA1. An ensemble of seven CMIP6 Earth system models is used to estimate future ozone concentrations. Projections are calculated for the years 2041-2060 and 2081-2100 under two different future scenarios (SSP2-4.5 and SSP3-7.0). The CMIP6 data is bias corrected with a univariate quantile delta mapping method, before being used in the statistical models.With respect to predictor selection, a sensitivity study is conducted, testing different sets of predictors to examine their influence on future ozone concentrations. Predictor sets with and without ozone, with only thermal and radiative predictors, and additionally thermo-dynamical or circulation-dynamical information are analyzed, in addition to the site-specific optimums described above.The projection results of the different predictor settings highlight the importance of ozone as predictor. In all predictor sets with included ozone, the results of the two scenarios SSP2-4.5 and SSP3-7.0 differ in their sign depending on the scenario used. The SSP2 scenario, called "Middle of the Road", leads to decreasing ozone concentrations in Europe, while the more pessimistic SSP3 scenario results in partly strong increases of harmful ground-level ozone concentrations. In contrast, all predictor sets that do not take ozone into account show consistently positive change signals.On the one hand, our results point to the need to include information on emission changes in statistical assessments in order to obtain a realistic picture of future ozone development. Furthermore, our research underscores the need to further reduce air pollution in Europe to better protect human health from direct emissions such as NOx and indirect pollutants such as ozone.

An adequate representation of the distribution of mosquitoes transmitting vector-based diseases under future climate change conditions is essential to estimate the occurrence of those diseases in areas such as the Mediterranean. For this purpose, species distribution models are used to establish a statistical relationship between recent and future environmental conditions and the spread of species. The 19 BioClim variables have been widely used to drive these models because they represent key features (e.g. hot, cold, dry, humid) of different temporal dimensions (quarter or month). The scientific community has paid less attention to the difficulties that arise when using BioClim variables, including how well the climate model can reproduce them. This study intended to assess the skill of 10 different high-resolution regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment’s CORE initiative that represent BioClim variables in Mediterranean regions. The skill analysis was implemented using the metrics of spatial correlation, spatial standard deviation and the mean absolute percentage error. The result reveals that REGCM4 and REMO15 are the best-performing RCMs in terms of standard deviation and the mean absolute percentage error, and RACMO and CCLM4 are the best-performing RCMs in terms of spatial correlation. The result shows that temperature-based BioClim variables are better represented by the majority of RCMs compared to precipitation models. A huge uncertainty remains when it comes to the representation of quarter-based variables. Hence, future studies should be focused on improving the representation of BioClim variables, especially those related to precipitation, by applying appropriate bias correction techniques. The best-performing RCMs of this study will be used to derive different species distribution models of Anopheles mosquitoes over the Mediterranean regions to estimate the future distribution of vector-based diseases.

Air pollution is repeatedly referred to as the largest environmental health risk in Europe. Exposure to heat can lead to a large variety of negative human health effects. Ground-level ozone (O3), one major air pollutant, often coincides with elevated air temperature levels. There is evidence that the resulting twofold exposure can lead to a human health risk beyond the sum of the individual effects of each health stressor (e.g., Hertig et al. 2020). But previous studies already point to the fact that the observed and modeled linkages between both variables as well as with underlying synoptic and meteorological drivers vary with the location of sites and from one region to another across Europe (e.g., Jahn, Hertig 2020; Jahn, Hertig 2022). Thus, impacts of concurrent elevated levels of O3 and air temperate on human health need to be considered with a region-specific perspective. Besides, disease-specific exposure-outcome-relationships have to be taken into account, also with respect to mitigation and adaption strategies in the context of climate change.The adverse human health effects of ground-level ozone and air temperature under recent and future climate conditions are analyzed and quantified by considering myocardial infarction (MI) as outcome variable. The urban area of Augsburg and two adjacent counties, located in a region with a strong and direct linkage between ozone and air temperature and a sharp projected increase of compound events, represent the regional focus. The association between both exposures and MI risk in Augsburg throughout the 21st century represents the particular focus of this study. The relationship between one health stressor alone and MI was already assessed in previous studies (e.g., Hertig 2020; Chen et al. 2019), but there is only limited evidence about the compound effect of both exposures on MI.Chen, K.; Breitner, S.; Wolf, K.; Hampel, R.; Meisinger, C.; et al. (2019) Temporal variations in the triggering of myocardial infarction by air temperature in Augsburg, Germany, 1987-2014. Eur Heart J. doi:10.1093/eurheartj/ehz116Hertig, E. (2020) Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Qual Atmos Health. doi: 10.1007/s11869-020-00811-zHertig, E.; Russo, A.; Trigo, R. (2020) Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271Jahn, S.; Hertig, E. (2022) Using clustering, statistical modeling, and climate change projections to analyze recent and future region-specific compound ozone and temperature burden over Europe. GeoHealth. Submitted.Jahn, S.; Hertig, E. (2020) Modeling and projecting health‐relevant combined ozone and temperature events in present and future Central European climate. Air Qual Atmos Health. doi: 10.1007/s11869‐020‐009610

Statistical models to evaluate the relationships between large-scale meteorological conditions, prevailing air pollution levels and combined ozone and temperature events, were developed during the 1993–2012 period with Central Europe as regional focus. Combined ozone and temperature events were defined based on the high frequency of coinciding, health-relevant elevated levels of daily maximum tropospheric ozone concentrations (based on running 8-h means) and daily maximum temperature values in the peak ozone and temperature season from April to September. By applying two different modeling approaches based on lasso, logistic regression, and multiple linear regression mean air temperatures at 850 hPa, ozone persistence, surface thermal radiation, geopotential heights at 850 hPa, meridional winds at 500 hPa, and relative humidity at 500 hPa were identified as main drivers of combined ozone and temperature events. Statistical downscaling projections until the end of the twenty-first century were assessed by using the output of seven models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Potential frequency shifts were evaluated by comparing the mid- (2031–2050) and late-century (2081–2100) time windows to the base period (1993–2012). A sharp increase of ozone-temperature events was projected under RCP4.5 and RCP8.5 scenario assumptions with respective multi-model mean changes of 8.94% and 16.84% as well as 13.33% and 37.52% for mid- and late-century European climate.

The aim of this project is to investigate the influence of health-relevant air-hygienic and climatological parameters on emergency room visits at the University Hospital of Augsburg. This is achieved by quantifying the effects of increased exposure to air substances and weather extremes.The emergency admission data from 2017 and 2018 are available as medical data basis. Among the air constituents, ozone, nitrogen dioxide and particulate matter values ​​are used, measured by several stations in Augsburg. The meteorological database contains the daily mean and daily maximum values ​​of several parameters such as air temperature, humidity, and wind. In addition, a catalog of different weather conditions was created. For this purpose, five Principal Component Analyzes were performed, one for each season and one for the entire year.The medical data set was broken down according to the seasons and days with specific meteorological conditions (e. g. heat days) or days that exceeded the WHO recommendations for air pollutants. These sub-divisions were undertaken in order to identify differences of the number of admissions under the occurrence of extreme days. To account for the possible delay between exposure and emergency cases, a lead time of up to seven days was included.The results so far show that in almost all subgroups of the ICD-10 classification there are highly significant correlations between the weather and air conditions and the number of emergency admissions. These occur almost exclusively on meteorological extreme days or when the WHO air pollution guideline values are exceeded. The strength of the correlations between the individual diagnoses and subgroups differs significantly. The proportion of age, gender and place of residence-specific differences can be classified as low. However, there are individual diseases in almost every main group that are showing very clear differences, sometimes even opposite correlations, between men and women or urban and suburban residents.The project is funded by the German Federal Environment Foundation and the German Research Foundation under - project number 408057478.

High concentrations of ground-level ozone (O3) and elevated levels of air temperature both represent natural hazards and health-relevant events. Two natural hazards occurring at the same time and contributing to a severe human health risk are defined as compound events. Co-occurring and hence compound ozone and temperature events pose a significant health risk and can lead to an intensified health burden for the European population (e.g., Hertig et al. 2020). Previous studies already point to the fact that the relationship of underlying main drivers with one or both hazards, their linkage as well as projected future frequency shifts of compound occurrences show spatial and temporal variations (e.g., Otero et al. 2016; Jahn, Hertig 2020). There is also evidence that compound events become more frequent in Europe during the 21st century due to climate change (e.g., Jahn, Hertig 2020; Hertig 2020). Consequently, recent and upcoming European protection and resilience strategies need to focus on region-specific current and future environmental and climatic conditions.In our current contribution we focus on health-relevant compound events by jointly evaluating elevated ground-level ozone concentrations and air temperature levels at a regional scale in Europe. A regionalization based on cluster analysis divides the European domain into regions of coherent ozone and temperature characteristics and variability. Spatiotemporally varying meteorological conditions which strongly influence the occurrence of compound events in the regions are identified. For projections until the end of the twenty-first century, the output of eight Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) is used.The results from this study show the regional character of ozone and temperature patterns and variabilities as well as respective recent and future compound event occurrences. The results can be used as a basis for further research to adjust and specify current air pollution and climate change mitigation and adaption strategies.Hertig, E. (2020) Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Qual. Atmos. Health. doi: 10.1007/s11869-020-00811-zHertig, E., Russo, A., Trigo, R. (2020) Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271Jahn, S., Hertig, E. (2020) Modeling and projecting health‐relevant combined ozone and temperature events in present and future Central European climate. Air Qual. Atmos. Health. doi: 10.1007/s11869‐020‐009610Otero N., Sillmann J., Schnell J.L., Rust H.W., Butler T. (2016) Synoptic and meteorological drivers of extreme ozone concentrations over Europe. Environ Res Lett. doi: 10.1088/ 1748-9326/11/2/024005

Analyses of the relationships between climate, air substances and health usually concentrate on urban environments due to increased urban temperatures, high levels of air pollution and the exposure of a large number of people compared to rural environments. Ongoing urbanization, demographic aging and climate change lead to an increased vulnerability with respect to climate-related extremes and air pollution. However, systematic analyses of the specific local-scale characteristics of health-relevant atmospheric conditions and compositions in urban environments are still scarce due to the lack of high-resolution monitoring networks. In recent years low-cost sensors became available, which potentially provide the opportunity to monitor atmospheric conditions with a high spatial resolution and which allow monitoring directly at exposed people.We develop a measurement system for several air substances like ozone, nitrogen oxides, carbon monoxide and particulate matter as well as meteorological variables like temperature and relative humidity, based on low-cost sensors. This involves the assembly of compact, weatherproof boxes with 3D-printed parts. They contain a control unit based on Arduino hardware to gather the sensor data as well as self-designed printed circuit boards (PCBs). A Pycom microcontroller is used for low-power, high-temporal data transmissions by Long-Term Evolution Cat-M1 (LTE-M). These Atmospheric Exposure Low-cost Monitoring units (AELCM) include digital and analogue sensors for air substances and meteorological variables, LCD display, RTC module, uninterruptible power supply, active ventilation, a SD Module as a data black box in addition to an optional internally running FTP server and optional GPS module. A computational fluid dynamics (CFD) simulation is used to evaluate the air flow inside the AELCM units. Sensors are selected based on own analyses as well as according to evaluation and performance in other projects. The measurement equipment is extensively tested using the high-quality measurement unit for meteorology and air substances (Atmospheric Exposure Monitoring System, AEMS) of our research group, located at the Augsburg University Hospital.

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. We also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

Within the context of analyzing daily heavy precipitation events in the Mediterranean under enhanced greenhouse gas forcing in the 21st century, a new method considering non-stationarities in the relationships of large-scale circulation predictors and regional precipitation extremes was applied. The Mediterranean area was split into up to 22 precipitation regions, and analyses were performed separately for 3 different seasons (autumn, winter and spring) and 3 different quantiles (90th, 95th and 99th). Estimations are based on a three-step censored quantile regression. Future estimations are performed by means of 3 model runs of the Max Planck Institute Earth System Model with Low Resolution (MPI-ESM-LR) for representative concentration pathways (RCPs) 4.5 and 8.5. Overall, the Mediterranean is mainly characterized by decreasing quantile values. Especially in the regions in the southeast, declines are significant, with up to 71.7% (-1.65 mm) in the Levante region (autumn) and over 16 mm (-38.2%) on Crete (winter). Increased precipitation quantiles were only assessed for a more or less extended region in the northern parts of the Central Mediterranean (winter and spring), for the northeastern coast of the Iberian Peninsula (autumn) and for northern Spain (spring). Overall, analyses showed that non-stationarities seriously affect precipitation behavior in most parts of the Mediterranean. Results indicated that 2 different regimes (western and eastern) inducing non-stationarities are predominant in the Mediterranean area. In autumn (winter), the western (eastern) regime is limited to the Iberian Peninsula (Levante), whereas in spring, the area of influence of both regimes is of equal size.

While many authors have described the adverse health effects of poor air quality and meteorological extremes, there remain inconsistencies on a regional scale as well as uncertainty about the single and joint effects of atmospheric predictors. In this context, we investigated the short-term impacts of weather and air quality on moderate extreme cancer-related mortality events for the urban area of Augsburg, Southern Germany, during the period 2000–2017. First, single effects were uncovered by applying a case-crossover routine. The overall impact was assessed by performing a Mann–Whitney U testing scheme. We then compared the results of this procedure to extreme noncancer-related mortality events. In a second step, we found periods with contemporaneous significant predictors and carried out an in-depth analysis of these joint-effect periods. We were interested in the atmospheric processes leading to the emergence of significant conditions. Hence, we applied the Principal Component Analysis to large-scale synoptic conditions during these periods. The results demonstrate a strong linkage between high-mortality events in cancer patients and significantly above-average levels of nitrogen dioxide (NO2) and particulate matter (PM2.5) during the late winter through spring period. These were mainly linked to northerly to easterly weak airflow under stable, high-pressure conditions. Especially in winter and spring, this can result in low temperatures and a ground-level increase and the accumulation of air pollution from heating and traffic as well as eastern lateral advection of polluted air. Additionally, above-average temperatures were shown to occur on the days before mortality events from mid-summer through fall, which was also caused by high-pressure conditions with weak wind flow and intense solar radiation. Our approach can be used to analyse medical data with epidemiological as well as climatological methods while providing a more vivid representation of the underlying atmospheric processes.

Air pollution and heat events present two major health risks, both already independently posing a significant threat to human health and life. High levels of ground-level ozone (O3) and air temperature often coincide due to the underlying physical relationships between both variables. The most severe health outcome is in general associated with the co-occurrence of both hazards (e.g. Hertig et al. 2020), since concurrent elevated levels of temperature and ozone concentrations represent a twofold exposure and can lead to a risk beyond the sum of the individual effects. Consequently, in the current contribution, a compound approach considering both hazards simultaneously as so-called ozone-temperature (o-t-)events is chosen by jointly analyzing elevated ground-level ozone concentrations and air temperature levels in Europe.Previous studies already point to the fact that the relationship of underlying synoptic and meteorological drivers with one or both of these health stressors as well as the correlation between both variables vary with the location of sites and seasons (e.g. Otero et al. 2016; Jahn, Hertig 2020). Therefore, a hierarchical clustering analysis is applied to objectively divide the study domain in regions of homogeneous, similar ground-level ozone and temperature characteristics (o-t-regions). Statistical models to assess the synoptic and large-scale meteorological mechanisms which represent main drivers of concurrent o-t-events are developed for each identified o-t-region.Compound elevated ozone concentration and air temperature events are expected to become more frequent due to climate change in many parts of Europe (e.g. Jahn, Hertig 2020; Hertig 2020). Statistical projections of potential frequency shifts of compound o-t-events until the end of the twenty-first century are assessed using the output of Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).Hertig, E. (2020) Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Qual. Atmos. Health. doi: 10.1007/s11869-020-00811-zHertig, E., Russo, A., Trigo, R. (2020) Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271Jahn, S., Hertig, E. (2020) Modeling and projecting health‐relevant combined ozone and temperature events in present and future Central European climate. Air Qual. Atmos. Health. doi: 10.1007/s11869‐020‐009610Otero N., Sillmann J., Schnell J.L., Rust H.W., Butler T. (2016) Synoptic and meteorological drivers of extreme ozone concentrations over Europe. Environ Res Lett. doi: 10.1088/ 1748-9326/11/2/024005

Lufttemperaturen, vor allem ihre Extreme wie Hitze- oder Kältewellen, haben einen signifikanten Einfluss auf die Mortalität. Es wurden verschiedene pathophysiologische Mechanismen identifiziert, die zu einer Erhöhung der Mortalität führen, vor allem in Bezug auf kardiovaskuläre und respiratorische Erkrankungen. Gegen Ende des 20. Jahrhunderts und Beginn des 21. Jahrhunderts ließen sich noch mehr Sterbefälle aufgrund von Kälteereignissen im Vergleich zu Hitzeereignissen beobachten, wobei sich dieses Verhältnis aufgrund des fortschreitenden Klimawandels deutlich in Richtung der hitzebedingten Mortalität verschiebt. Sowohl bei Hitze- als auch Kälteextremen ist die Erhöhung der Mortalität in älteren Bevölkerungsgruppen besonders ausgeprägt, sodass demografische Veränderungen im Hinblick auf „alternde Gesellschaften“ das Risiko weiter verschärfen werden.

Influences of air substances and meteorological conditions on human healthFor a long time it has been known that exceptionally strong and long-lasting heat waves have negative health effects on the population, which is expressed in an intensification of existing diseases and over-mortality of certain risk groups (Kampa, Castanas 2008). Often associated with heat are stagnant airflow conditions that cause a large increase in the concentration of certain air substances (Ebi, McGregor 2008). Many of these air substances have a strong adverse effect on the human organism (Kampa, Castanas 2008).The aim of the project is to investigate the actual hazard potential air pollution- and climatological variables by quantifying the effects on human health of increased exposure to air constituents and temperature extremes. Different multivariate statistical methods such as correlation analysis, regression models and random forests, extreme value analysis and individual case studies are used.As a medical data basis for this purpose, the emergency department data of the University Hospital Augsburg are regarded. In addition to the diagnosis, supplementary information such as age, gender, place of residence and pre-existing conditions of the patients are used. Among the air constituents, the focus is on ozone, nitrogen dioxide and particulate matter. In the meteorological part, the focus is primarily on temperature, which is not only a direct burden but, as in the case of ozone, also has a decisive influence on the formation of ground-level ozone. However, a large number of other meteorological parameters such as precipitation, relative humidity and wind speed as well as the synoptic situation also play a major role in the formation, decomposition process and the distribution of pollutants (Ebi, McGregor 2008).The first major question to answer is whether air pollution and meteorological stress situations are visible in the emergency department data. Further in-depth questions are which factors have the greatest negative impact, what is the most common environment-related disease, which weather conditions carry a higher than average risk and what are the health risks of climate change.Ideally, the analysis may also provide a short-term forecast from which to derive whether or not there will be an above or below average number of visits to the emergency department.The project is funded by the German Federal Foundation for Environment (DBU) and the German Research Foundation (DFG) - project number 408057478.Literature:Nuvolone D., Petri D., Voller F. (2017): The effects of ozone on human health. doi: 10.1007/s11356-017-9239-3.Requia W., Adams M., Arain A., Papatheodorou S., Koutrakis P., Mahmoud M. (2018): Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables. doi: 10.2105/AJPH.2017.303839Xing Y., Xu Y., Shi M., Lian Y. (2016): The impact of PM2.5 on the human respiratory system. doi: 10.3978/j.issn.2072-1439.2016.01.19

Temperature extremes and air pollution pose a significant threat to human health. A specific concern applies to heat events and elevated ground-level ozone concentrations, due to the physical relationships between these variables, the single and combined effects of both variables on human health and the anticipated substantial changes in the scope of climate change.The present contribution addresses relationships between air temperature and ground-level ozone, the association of these variables with atmospheric circulation patterns, the anticipated changes under future climate change as well as their association with human morbidity (i.e. myocardial infarction frequencies, Hertig et al. 2019) and mortality. The focus is on two climatically different regions in Europe, i.e., Bavaria (Central Europe) and Portugal (South Europe).In general, a strong relationship between air temperature and ozone formation became evident. Due to the non-linear nature of the relationship, higher temperatures usually led to substantially enhanced ozone concentrations. In the scope of climate change, considerable increases of maximum temperatures were assessed for Bavaria until the end of the century. Also, future ozone concentrations were projected to rise (Hertig 2020). With respect to spell-length related extremes (heat waves and/ or ozone pollution waves), heat waves were identified as the most frequent wave type for the two European regions under investigation. Waves were associated with in-situ built-up as well as with advection of air masses. Despite different climate settings, a comparable exposure to heat and ozone waves was found in Central and South Europe. In view of excess mortality, the most severe impacts were always associated with compound heat-ozone waves (Hertig et al. 2020).Research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 408057478.Hertig, E., Russo, A., Trigo, R. (2020): Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271Hertig, E. (2020): Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Quality, Atmosphere and Health. DOI: https://doi.org/10.1007/s11869-020-00811-zHertig, E., Schneider, A., Peters, A., von Scheidt, W., Kuch, B., Meisinger, Ch. (2019): Association of ground-level ozone, meteorological factors and weather types with daily myocardial infarction frequencies in Augsburg, Southern Germany. Atmos. Environment. DOI: 10.1016/j.atmosenv.2019.116975

Temperature extremes like hot days or prolonged episodes of high air temperature like heat waves can cause adverse human health effects. Heat-related mortality only represents the extreme end of a variety of possible health outcomes like heat exhaustion or heat stroke.Exposure to ground-level ozone provokes negative impacts on human health primarily affecting the cardio-pulmonary system causing respiratory or cardiovascular diseases. These diseases include, but are not limited to, lung inflammation and tissue damage, asthma, heart attacks or heart failure.High levels of ozone and temperature often coincide due to the underlying ozone formation characteristics. As synergistic effects lead to a risk beyond the sum of their individual effects, the co-occurrence of elevated levels of air temperature and ground-level ozone concentrations represents an even intensified human health risk.The current contribution deals with statistical models and analysis of the interplay between large-scale meteorological and synoptic conditions, prevailing air pollution levels and combined ozone and temperature events under present and future climatic conditions. In this context, meteorological mechanisms representing main drivers of these concurrent ozone and temperature events were identified. Large-scale atmospheric circulation dynamics and their relationships with ground-level ozone and temperature conditions were evaluated.The methodological focus was primary on statistical modeling approaches and different machine learning methods. Self-Organizing Maps, an artificial neural network algorithm based on unsupervised machine learning, were used to classify synoptic types based on daily mean sea level pressure reanalysis data. The resulting synoptic types were evaluated with regard to the European ozone and temperature characteristics in order to identify types associated with high ozone and temperature. Regression analyses with e.g. shrinking methods were used to identify main predictors for concurrent ozone and temperature events. Due to data availability and research foci, two varying time windows from 1993 to 2012 as well as from 2004 to 2018 were used within the study. The European area built the regional focus.Anthropogenic-induced global climate change affects not only mean but also extreme temperatures as well as associated ground-level ozone concentrations due to changing synoptic circulation and chemical environment conditions. Future frequency changes of concurrent ozone and temperature events were evaluated exemplarily for Central Europe. Statistical downscaling projections until the end of the twenty-first century were assessed by using the output of seven models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). A sharp increase was projected under RCP4.5 and RCP8.5 scenario assumptions. Respective multi-model mean changes amounted to 8.94% and 16.84% as well as 13.33% and 37.52% for mid- (2031–2050) and late-century (2081–2100) European climate, respectively (Jahn and Hertig 2020). Hotspot regions with more frequent occurrences of these combined events in Central Europe were identified for which, due to their associated individual and combined health effects, a higher future vulnerability can be expected.

Within the scope of urban climate modeling, weather analogs are used to downscale large-scale reanalysis-based information to station time series. Two novel approaches of weather analogs are introduced which allow a day-by-day comparison with observations within the validation period and which are easily adaptable to future periods for projections. Both methods affect the first level of analogy which is usually based on selection of circulation patterns. First, the time series were bias corrected and detrended before subsamples were determined for each specific day of interest. Subsequently, the normal vector of the standardized regression planes (NVEC) or the center of gravity (COG) of the normalized absolute circulation patterns was used to determine a point within an artificial coordinate system for each day. The day(s) which exhibit(s) the least absolute distance(s) between the artificial points of the day of interest and the days of the subsample is/are used as analog or subsample for the second level of analogy, respectively. Here, the second level of analogy is a second selection process based on the comparison of gridded temperature data between the analog subsample and the day of interest. After the analog selection process, the trends of the observation were added to the analog time series. With respect to air temperature and the exceedance of the 90th temperature quantile, the present study compares the performance of both analog methods with an already existing analog method and a multiple linear regression. Results show that both novel analog approaches can keep up with existing methods. One shortcoming of the methods presented here is that they are limited to local or small regional applications. In contrast, less pre-processing and the small domain size of the circulation patterns lead to low computational costs.

Air pollution and hot temperatures present two major health risks, especially for vulnerable groups such as children, the elderly, and people with pre-existing conditions. Episodes of high ozone concentrations and heat waves have been registered throughout Europe and are expected to continue to grow due to climate change. Here, several different heat and ozone wave definitions were applied to characterize the wave-type extremes for two climatically different regions, i.e., Portugal (South Europe) and Bavaria (Central Europe), and their impacts were evaluated considering each type of hazard independently but also when they occur simultaneously. Heat and ozone waves were analyzed with respect to the underlying atmospheric circulation patterns and in terms of their association with human mortality. Heat waves were identified as the most frequent wave type and, despite different climate settings, a comparable exposure to heat and ozone waves was found in Central and South Europe. Waves were associated with in-situ built-up as well as with advection of air masses. However, in Bavaria waves showed the strongest connection with autochthonous weather conditions, while for Portugal, the strongest relationship appeared for eastern and north-eastern inflow. The most severe events, as measured by excess mortality, were always associated to compound heat-ozone waves.

The Indus River system originates within high mountain ranges of Hindukush, Karakoram and Himalayans (HKH) and contains the largest cryosphere outside the Polar Regions. It assures livelihood of millions of people, before descending into the Arabian Sea. Different processes, which involve complex interplays of contrasting synoptic-scale circulations and regional topography, largely govern precipitation, which varies significantly with space-time and altitudes in upper Indus basin (UIB). In contrast, the Lower Indus (LI) has arid to semi-arid climate and depends heavily on melt-dominated water supply from the UIB. Considering climate hotspot nature of this basin, a pragmatic assessment of future precipitation and temperature changes at basin-scale are fundamental to provide effective policy advice.However, long-term, reliable and consistent data to effectively simulate orographic climatology within UIB that largely governs the basin hydrology is scarce. Consequently, even the mean direction of regional climate is highly controversial and ranging from rapidly retreating glaciers to the so-called “Karakoram anomaly”. While the provision of additional useful data is still an ongoing process, improvements in simulation methodologies using the available observational network, can still offer some opportunities to reduce uncertainties. One way is to make use of large-scale atmospheric circulations, which are modeled more reliably than precipitation itself. Moreover, the circulation-precipitation relationships can additionally explain governing mechanisms to improve confidence in resulting simulations.In our study, we modeled observed precipitation and temperature (Tmax and Tmin) dynamics of the entire basin. A seasonally and spatially differentiated analysis was done using improved UIB monitoring, which provide enhanced spatio-altitudinal information. By taking advantage of the recent high-altitudes (HA) installations within UIB, we argue that precipitation at relatively low-altitudes only quantitatively differ from HA rates, but share a significant joint variability at sub-regional scales. Therefore, the low-altitude stations (historic) can provide reasonable inferences about more uncertain orographic structure of UIB. We adapted generalized linear models (GLMs) with Tweedie and Gamma distributions to model precipitation and multiple linear regressions (MLRs) for temperature simulations using time-series of carefully selected regionally representatives, as predictand and principal component scores of different larger-scale dynamical and thermodynamic variables from ERA-Interim reanalysis, as predictors. The final regression models, which were identified through a cross validation framework, showed significant statistical skills and physical consistency to simulate observed seasonal precipitation and temperature variability over larger spatio-altitudinal scales. We further used the predictors to identify better performing regional and seasonal CIMP5- GCMs by comparing predictors through Taylor diagrams in the historical period. ERA-Interim predictors served as a basis for evaluation. Reanalysis uncertainties were assessed by using also NCEP-NCAR-II and ERA5 reanalysis. We considered two radiative forcings (RCP4.5 and RCP8.5) to analyze median change signals of precipitation (temperature) during mid (2041-2070) and end of 21st century (2071-2100). The signal to noise ratio was computed to evaluate future changes compared to observed natural variability.

An increasing fraction of people living in urban areas and the expected increase in long lasting heat waves highlight the important role of urban climates in terms of future climate change impacts, especially with relation to the heat-health sector. Due to the urban heat island (UHI) effect and its (generally) increased intensity particularly during nighttime, people living in urban areas happen to be more affected by heat-related discomfort and health risks than those in non-urban regions. In this contribution, temperatures of both rural and urban sites (station couples) in Switzerland and Southern Germany are analyzed, using (i) observed as well as (ii) bias-corrected and downscaled climate model data for daily minimum (tmin) and daily maximum temperature (tmax) to account for the UHI in future climates. As meteorological data are often restricted to locations of long-term measurements at rural sites only, they need to be transferred to urban sites first. For this purpose, the well-established quantile mapping technique (QM) is tested in a two-step manner. The resulting products are urban time series at daily resolution for tmin and tmax. By analyzing the temperature differences of the observed climate at rural sites and their respective urban counterparts and by assuming a stationary relationship between both, we can represent the UHI in future climates, which is quantified in terms of heat indices based on tmin and tmax (tropical nights, summer days, hot days).The QM performance is evaluated using long-term weather station data of a Zurich station couple in a comprehensive cross-validation framework. Results reveal a promising performance in the present-day climate, given very low biases in the validation.Applying the proposed method to the employed station couples, projections indicate distinct urban-rural temperature differences (UHI) during nighttime (considering the frequency of tropical nights based on tmin) compared to weak differences during the day (considering the frequency of summer days and hot days based on tmax). Moreover, scenarios suggest the frequency of all indices to dramatically rise at the urban site by the end of the century under a strong emission scenario (RCP8.5): compared to the rural site, the number of tropical nights almost doubles while the number of summer days reveals about 15% more days at the urban site when focusing on the station couple in Zurich and the late scenario period. The lack of nighttime relief, indicated by tmin not falling below 20°C (i.e. a tropical night), is especially problematic in terms of human health and makes the study of the urban climate in general and the UHI effect in particular indispensable.

Under enhanced anthropogenic greenhouse gas forcing heat waves are only one example of climatic risks mankind has to deal with. Especially in urban areas where most of the people will live until the end of the 21st century heat waves are a serious risk factor since the urban heat island will reinforce such events. For the city of Augsburg, new analog methods are utilized for assessing the development and impacts of heat waves taking into account the varying urban structure.For model calibration the temperature data from the Augsburg-Mühlhausen weather station operated by the German Weather Service (DWD) and atmospheric circulation variables of the ERA5 reanalysis data set were used to analyze the recent temperature development. For this purpose, the least deviation of the normal vector was used to determine a subsample of analogs corresponding to the day of interest. The normal vector was derived from the regression plane of the prevailing circulation on the respective day. Subsequently, the temperature patterns were used to define the analog day from the subsample. For future periods, the same method was applied to model data for two representative concentration pathways (RCP4.5, RCP8.5) of different general circulation models (GCM: ACCESS1-0, CNRM-CM5, MPI-ESM-LR). Thus, we derive future time series of analogs corresponding to events prevailing in the observational period. To account for projected trends of the GCMs, the trends of all time-series were first removed and, after the analog selection process, added again according to the trends of the GCMs.Temperature extremes are defined as days with temperatures exceeding the 90th quantile (Q90) and heat days are defined as days where at least two temperature indices (TMIN, TMEAN, TMAX) exceed Q90. When at least three consecutive days are defined as heat day a heat wave is proclaimed. Analysis have shown that under consideration of RCP8.5 (RCP4.5) and all model runs the number of heat days in the end of the 21st century will be nine (five) times higher than within the reference period 1970-2000. Furthermore, the mean duration of heatwaves will extend by factor four (two), whereby heat waves of more than 30 (15) consecutive days are possible.

Air pollution as well as high air temperature both pose a large risk to human health in Europe. High temperature levels are associated with an exceptionally high mortality rate, only representing the extreme end of a wide range of possible health effects. Tropospheric ozone, a secondary air pollutant, is primarily built by photochemical reactions under solar radiation with the involvement of precursor gases including nitrogen oxides, carbon monoxide, methane, and non-methane volatile organic compounds. Due to the specific characteristics of ozone formation, high levels of ozone and temperature often coincide, posing an even intensified threat to human health.The current scientific work focuses on the co-occurrence of these two health stressors as well as their underlying meteorological conditions. A subset of European ozone (AirBase_v8, EEA) and temperature (ECA&D) stations is selected for analysis based on individual station locations and data coverage. Taking into account different settings of air substances concentrations (urban, outer conurbation area, rural regions), these stations are classified and grouped by station type and area type resulting in five distinct station classes: urban traffic, urban background, suburban background, rural background and rural industrial.Maximum daily 8-hour average ozone values (MDA8O3, EEA), observed daily maximum air temperatures (TX, ECA&D) and meteorological variables (from ERA5, ECMWF) form the data basis for model building. Current thresholds and extreme definitions e.g. based on WHO air quality guidelines or high percentiles (75th and 90th) are examined and discussed to describe elevated levels of these variables and to finally define combined ozone-temperature events.Possible regional patterns as well as disparities between urban and rural areas regarding the specific settings for ozone formation as well as varying meteorological mechanisms for the occurrence of combined ozone-temperature events are closely examined. The methodological focus is primary on statistical modelling, the application and comparison of varying multivariate statistical approaches and different machine learning methods, e.g. various regression analyses using shrinkage methods or random forests. Consequently, statistical models are generated to analyse the influence of meteorological conditions on the occurrence of combined ground-level ozone and temperature events along with the identification of primary key factors (e.g. ozone persistence or larger-scale air temperature and wind conditions) at each specific location.Furthermore, frequency and intensity changes of combined ozone-temperate events in the scope of global warming are assessed. Thus, projections of these co-occurring events under the constraints of ongoing climate change until the end of the 21st century are analysed by integrating projections of general circulation models into the statistical modelling process.

Our climate is changing at an unprecedented rate. This not only has impacts on the planet and the ecosystem, with extinction rates rising rapidly, but also on human health (Figure 1). The effects of climate change are and will continue to disrupt the basic requirements for health such as clean water, clean air, adequate food and will furthermore exacerbate underlying social, economic, and ecological.

The machine learning models (MLMs), including support vector regression (SVR), multivariate adaptive regression spline (MARS), boosted regression trees (BRT), and projection pursuit regression (PPR) are compared to traditional method i.e. nonlinear regression (NLR) in regional flood frequency analysis (RFFA). In this study, the Karun and Karkheh watersheds, which is located in the southwestern of Iran, with the same climatic and physiographic conditions are considered. Fifty-four hydrometric stations with a period of 21 years (1993–2013) are selected based on the instructions of U.S. Federal Agencies Bulletin 17 B were applied for RFFA. The generalized normal (GNO) probability distribution function (PDF) is selected by the L-moment method among five PDFs, including the GNO, generalized Pareto (GP), generalized logistic (GL), generalized extreme value (GEV) and Pearson type 3 (P ІІІ) to estimate flood discharge for the expected return periods. Twenty-five predictor variables, such as physiographic, climatologic, geologic, soil and land use variables are extracted. Follow land, maximum 24-h rainfall, mean watershed slope, compactness coefficient, mean and maximum watershed elevation variables are recognized as the appropriate combination of input using gamma test (GT). The overall results indicate that the SVR, PPR, and MARS models in comparison to the NLR and BRT models have a better performance to estimate flood discharge with the expected return periods. Future, the SVR model based on radial basis function (RBF) kernel is chosen as the best model in terms of the mean of the Nash-Sutcliff coefficient (M-Ef) and the mean of relative root mean squared error (M-RMSEr) (i.e. 0.94 and 63.93, respectively) for different return periods.

Relationships of larger scale meteorological predictors with ground-level daily maximum ozone (O3max) and daily maximum air temperature (Tmax) for stations in Bavaria were analysed. O3max and Tmax as well as threshold exceedances of these variables were assessed under the constraints of ongoing climate change until the end of the twenty-first century. Under RCP8.5 scenario conditions, a substantial increase of Tmax in the months from April to September arose, with a mean value of 5 K in the period 2081–2100 compared with the historical period 1986–2005. Statistical downscaling projections pointed to a mean O3max rise of 17 μg/m3. The frequency of threshold exceedances showed also large changes. Hot days may occur in the future at about 30% of all days. Exceedances of O3max > 100 μg/m3 were projected to increase to about 40% of all days at urban traffic sites and up to about 70% in the rural regional background. Days with O3max > 120 μg/m3 occurred still at about 20% of all days at urban traffic sites and at about 45% in rural regional background locations. With respect to combined Tmax > 30 °C and O3max > 100 μg/m3 events in the future, an occurrence of such events at about 27–29% of all days in the summer months from April to September was assessed. The increases were mainly associated with the strong temperature rise until the end of the century. In summary, the projected Tmax and O3max changes point to a considerable increased health burden in Bavaria until the end of the century, resulting from strong changes of both variables and their associated individual and combined impact on human health.

Long droughts periods can affect the Mediterranean region during the winter season, when most of annual precipitation occurs, and consequently have strong impacts on agriculture, groundwater levels and water resources. The goal of this study is to model annual maximum dry spells lengths (AMDSL) that occur during the extended winter season (October to April). The spatial patterns of extreme dry spells and their relationships with large-scale atmospheric circulation were first investigated. Then, AMDSL were modelled using Generalized Extreme Value (GEV) distributions incorporating climatic covariates, to evaluate the dependences of extreme dry spells to synoptic patterns using an analogue approach. The data from a network of 160 rain gauges having daily precipitation measurements between 1960 and 2009 are considered together with the ERA-20C reanalysis of the 20th century to provide atmospheric variables (geopotential heights, humidity, winds). A regional classification of both the occurrence and the duration of AMDSL helped to distinguish three spatially contiguous regions in which the regional distributions were found homogeneous. From composite analysis, significant positive anomalies in geopotential height (Z500) and negative anomalies in zonal wind (U850) and relative and specific humidity (S850, R850) were found to be associated with AMDSL in the three regions and provided the reference to build analogue days. Finally, non-stationary GEV models have been compared, in which the location and scale parameters are related to different atmospheric indices. Results indicates, at the whole Mediterranean scale, that positives anomalies of the North Atlantic Oscillation index and to a lesser extent the Mediterranean Oscillation index are linked to longer extreme dry spells in the majority of stations. For the three regions identified, the frequency of U850 negative anomalies over North Africa is significantly associated with the magnitude of AMDSL. AMDL are also associated with the frequency of S850 negative anomalies for the southeastern region, and with positive Z500 anomalies for the Western and North-eastern Mediterranean regions.

Temporal variability is an important feature of climate, comprising systematic variations such as the annual cycle, as well as residual temporal variations such as short-term variations, spells and variability from interannual to long-term trends. The EU-COST Action VALUE developed a comprehensive framework to evaluate downscaling methods. Here we present the evaluation of the perfect predictor experiment for temporal variability. Overall, the behaviour of the different approaches turned out to be as expected from their structure and implementation. The chosen regional climate model adds value to reanalysis data for most considered aspects, for all seasons and for both temperature and precipitation. Bias correction methods do not directly modify temporal variability apart from the annual cycle. However, wet day corrections substantially improve transition probabilities and spell length distributions, whereas interannual variability is in some cases deteriorated by quantile mapping. The performance of perfect prognosis (PP) statistical downscaling methods varies strongly from aspect to aspect and method to method, and depends strongly on the predictor choice. Unconditional weather generators tend to perform well for the aspects they have been calibrated for, but underrepresent long spells and interannual variability. Long-term temperature trends of the driving model are essentially unchanged by bias correction methods. If precipitation trends are not well simulated by the driving model, bias correction further deteriorates these trends. The performance of PP methods to simulate trends depends strongly on the chosen predictors., EU COST Association, Grant/Award Number: ES1102; Ministry of Education, Youth, and Sports of the Czech Republic, Grant/AwardNumber:LD12029

In the context of analyzing temporal varying relationships of heavy precipitation events in the Mediterranean area and associated anomalies of the large-scale circulation, quantile regression models were established. The models were calibrated using different circulation and thermodynamic variables at the 700 hPa and 850 hPa levels as predictors as well as daily precipitation time series at different stations in the Mediterranean area as predictand. Analyses were done for the second half of the 20th century. In the scope of assessing non-stationarities in the predictor-predictand relationships the time series were divided into calibration and validation periods. 100 randomized subsamples were used to calibrate/validate the models under stationary conditions. The highest and lowest skill score of the 100 random samples was used to determine the range of random variability. The model performance under non-stationary conditions was derived from the skill scores of cross-validated running subintervals. If the skill scores of several consecutive years are outside the range of random variability a non-stationarity was declaimed. Particularly the Iberian Peninsula and the Levant region were affected by non-stationarities, the former with significant positive deviations of the skill scores, the latter with significant negative deviations. By means of a case study for the Levant region we determined three possible reasons for non-stationary behavior in the predictor-predictand relationships. The Mediterranean Oscillation as a superordinate system affects the cyclone activity in the Mediterranean basin and the location and intensity of the Cyprus low. Overall, it is demonstrated that non-stationarities have to be taken into account within statistical downscaling model development.

Climate change projections are subject to uncertainty arising from climate model deficiencies, unknown initial conditions and scenario assumptions. In the IPCC reports and many other publications climate changes and uncertainty ranges are usually displayed in terms of multi-model ensemble means and confidence intervals, respectively. In this study, we present a more quantitative assessment and statistical testing of climate change signals in the light of uncertainty. The approach is based on a two-way analysis of variance, referring to 24 climate models from the CMIP3 multi-model ensemble, and extents over the 21st century. The method also distinguishes between different climate variables, time scales and emission scenarios and is combined with a simple bias correction algorithm. The Mediterranean region has been chosen as a case study because it represents an assumed hot spot of future climate change, where temperature is projected to rise substantially and precipitation may decrease dramatically by the end of the 21st century. It is found that future temperature variations are mainly determined by radiative forcing, accounting for up to 60% of total variability, especially in the western Mediterranean Basin. In contrast, future precipitation variability is almost completely attributable to model uncertainty and model internal variability, both being important in more or less equal shares. This general finding is slightly depending on the prescribed emission scenario and strictly sensitive to the considered time scale. In contrast to precipitation, the temperature signal can be enhanced noticeably when bias-correcting the models' climatology during the 20th century: the greenhouse signal then accounts for up to 75% of total temperature variability in the regional mean.

Special Issue: VALUE: Validating and Integrating Downscaling Methods for Climate Change Research., Credible information about the properties and changes of extreme events on the regional and local scales is of prime importance in the context of future climate change. Within the EU-COST Action VALUE a comprehensive validation framework for downscaling methods has been developed. Here we present validation results for extremes of temperature and precipitation from the perfect predictor experiment that uses reanalysis-based predictors to isolate downscaling skill. The raw reanalysis output reveals that there is mostly a large bias with respect to the extreme index values at the considered stations across Europe, clearly pointing to the necessity of downscaling. The performance of the downscaling methods is closely linked to their specific structure and setup. All methods using parametric distributions require non-standard distributions to correctly represent marginal aspects of extremes. Also, the performance is much improved by explicitly including a seasonal component, particularly in case of precipitation. With respect to the marginal aspects of extremes the best performance is found for model output statistics (MOS), weather generators (WGs) as well as perfect prognosis (PP) methods using analogues. Spell-length-related extremes of temperature are best assessed by MOS and WGs, spell-length-related extremes of precipitation by MOS and PP methods using analogues. The skill of PP methods with transfer functions varies strongly across the methods and depends on the extreme index, region and season considered., This work has been carried out as part of the EU‐COST Action VALUE (ES1102). The authors are particularly grateful for the instructive and very helpful comments of two anonymous reviewers. P.M.M.S. thanks the Portuguese Foundation for Science and Technology (FCT) for funding under Project SOLAR‐PTDC/GEOMET/7078/2014.