Suchergebnisse

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.

49 pags, 10 figs, 2 tabs. -- Supplementary data is available at the Publisher's web ; The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty. ; A portion of the work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the NASA Aeronautics and Space Administration. A portion of the work was carried out the National Center for Atmospheric Research, which is operated by the University Corporation for Atmospheric Research under sponsorship of the National Science Foundation. PY acknowledges support from the Faculty of Science and Technology, Lancaster University. JB and UI acknowledge NordForsk under the Nordic Programme on Health and Welfare Project #75007: Understanding the link between air pollution and distribution of related health impacts and welfare in the Nordic countries (Nordic Welf Air); and the H2020-LCE project: Role of technologies in an energy efficient economy – model based analysis policy measures and transformation pathways to a sustainable energy system (REEEM), Grant agreement no.: 691739. GZ acknowledges the New Zealand Government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N027736/1] and the Natural Environment Research Council [grant number NE/N003411/1]. ; Peer reviewed