Suchergebnisse

Ozon ist ein Luftschadstoff mit negativen Auswirkungen auf die menschliche Gesundheit und auf die Vegetation. Seit den 1990er Jahre sind die Ozonkonzentrationen in Europa aufgrund von Emissionsreduzierungen zurückgegangen. Der Klimawandel wird jedoch die Bedingungen, die die Ozonbildung begünstigen, verstärken, was zu mehr Überschreitungen der Ozonrichtwerte führen kann. Wir haben den Einsatz von Methoden des Maschinellen Lernens (ML) untersucht, um Beziehungen zwischen jährlichen oder saisonalen Klimakennzahlen und der jährlichen Anzahl von Überschreitungen eines Ozonschwellenwertes für Messstandorte in Deutschland im Zeitraum 1995 − 2018 herzustellen, wobei Messungen aus den Messnetzen der Bundesländer und des UBAs und COSMO-REA6 meteorologische Daten verwendet wurden. Die Vorhersagewerte (Prädiktand) waren die Anzahl der Tage pro Jahr, an denen der höchste tägliche 8-Stunden-Mittelwert (MDA8) von 120 μg/m3 überschritten wurden (Ozonüberschreitungen), und die Anzahl der Episoden (zwei oder mehr aufeinander folgende Tage mit einer Ozonüberschreitung). Als Variablen (Prädiktoren) wurden unterschiedliche Klimakennzahlen (z. B. Anzahl Sommertage/Trockentage/tropische Nächte, Indikatoren zu Windrichtung und relativer Feuchte) und Stationsmerkmale verwendet. Für das Maschinelle Lernen wurde H2O AutoML verwendet. Es wurden getrennte ML-Modelle für normale (weniger als 55 Überschreitungen pro Jahr) und extreme Werte (55 oder mehr Überschreitungen) entwickelt. Für die normalen Werte waren Temperaturindikatoren (Anzahl der tropischen Nächte/ Sommertage) neben Informationen über den Breitengrad, die Stationshöhe und die Stationsklassifizierung die einflussreichsten Variablen. Bei den extremen Werten wurden die Auswirkungen von Strahlung, relativer Luftfeuchte und Wind dominanter. Für die normalen Werte war die Güte der ML-Modelle angemessen, für die extremen Werte waren die Ergebnisse jedoch weniger robust. Bei den ML-Modellen handelte es sich um sogenannte "relationship fitting" Modelle, die auf neue Jahre und Stationen angewendet werden können, aber nicht für Emissionsszenarien. Sie sind weniger gut für die genaue Vorhersage extremer Bedingungen geeignet. Es ist zu empfehlen, die Klimadatensätze monatlich oder saisonal aggregiert statt jährlich aggregiert zu speichern. Es sollte zudem ein Indikator für die relative Luftfeuchte festgelegt werden, weil diese Variable eine wichtige Rolle in Prozessen im Bereich der Luftqualität spielt.

An accurate simulation of the absorption properties is key for assessing the radiative effects of aerosol on meteorology and climate. The representation of how chemical species are mixed inside the particles (the mixing state) is one of the major uncertainty factors in the assessment of these effects. Here we compare aerosol optical properties simulations over Europe and North America, coordinated in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII), to 1 year of AERONET sunphotometer retrievals, in an attempt to identify a mixing state representation that better reproduces the observed single scattering albedo and its spectral variation. We use a single post-processing tool (FlexAOD) to derive aerosol optical properties from simulated aerosol speciation profiles, and focus on the absorption enhancement of black carbon when it is internally mixed with more scattering material, discarding from the analysis scenes dominated by dust. We found that the single scattering albedo at 440nm (omega(0,440)) is on average overestimated (underestimated) by 3-5% when external (core-shell internal) mixing of particles is assumed, a bias comparable in magnitude with the typical variability of the quantity. The (unphysical) homogeneous internal mixing assumption underestimates omega(0,440) by 14%. The combination of external and core-shell configurations (partial internal mixing), parameterized using a simplified function of air mass aging, reduces the omega(0,440) bias to-1/-3%. The black carbon absorption enhancement (E-abs) in core-shell with respect to the externally mixed state is in the range 1.8-2.5, which is above the currently most accepted upper limit of 1.5. The partial internal mixing reduces E-abs to values more consistent with this limit. However, the spectral dependence of the absorption is not well reproduced, and the absorption Angstrom exponent AAE(675)(440) is overestimated by 70-120%. Further testing against more comprehensive campaign data, including a ...

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071–2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041–2070 and 2071–2100 time windows, respectively.

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071-2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041-2070 and 2071-2100 time windows, respectively.

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071-2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041-2070 and 2071-2100 time windows, respectively.

Ozone air pollution is identified as one of the main threats bearing upon human health and ecosystems, with 25 000 deaths in 2005 attributed to surface ozone in Europe (IIASA 2013 TSAP Report #10). In addition, there is a concern that climate change could negate ozone pollution mitigation strategies, making them insufficient over the long run and jeopardising chances to meet the long term objective set by the European Union Directive of 2008 (Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008) (60 ppbv, daily maximum). This effect has been termed the ozone climate penalty. One way of assessing this climate penalty is by driving chemistry-transport models with future climate projections while holding the ozone precursor emissions constant (although the climate penalty may also be influenced by changes in emission of precursors). Here we present an analysis of the robustness of the climate penalty in Europe across time periods and scenarios by analysing the databases underlying 11 articles published on the topic since 2007, i.e. a total of 25 model projections. This substantial body of literature has never been explored to assess the uncertainty and robustness of the climate ozone penalty because of the use of different scenarios, time periods and ozone metrics. Despite the variability of model design and setup in this database of 25 model projection, the present meta-analysis demonstrates the significance and robustness of the impact of climate change on European surface ozone with a latitudinal gradient from a penalty bearing upon large parts of continental Europe and a benefit over the North Atlantic region of the domain. Future climate scenarios present a penalty for summertime (JJA) surface ozone by the end of the century (2071-2100) of at most 5 ppbv. Over European land surfaces, the 95% confidence interval of JJA ozone change is [0.44; 0.64] and [0.99; 1.50] ppbv for the 2041-2070 and 2071-2100 time windows, respectively.