Aircraft based measurements of atmospheric Sulfur Dioxide and ground based measurements of gaseous Sulfur (VI) in the simulated internal flow of an aircraft engine: Implications for atmospheric aerosol formation

This dissertation is concerned with aircraft based measurements of sulfur dioxide in the atmosphere and gaseous sulfuric acid measurements in the internal flow of an aircraft gas turbine engine. Both trace gases promote the formation and growth of sulfate aerosol particles which play an important role in the chemistry of the troposphere and perhaps even in climate. An Ion Trap Mass Spectrometer specially adapted for in flight measurements was employed in the aircraft campaign. Several high resolution altitude profiles in polluted and un-polluted tropospheric air were obtained and implications on particle formation and growth was examined. Sulfuric acid was also measured in the simulated internal flow of an aircraft engine with the Ion Trap Mass Spectrometer. The sulfur conversion efficiency epsilon was determined for three different fuel sulfur contents and two combustor operating conditions. The results suggest that modern aircraft engines have conversion efficiencies in the range of a few percent (2.3 ±1.2 %) and that modern engines have larger conversion efficiencies compared to the old engines. Even such low epsilon allow the formation and growth of volatile aerosol particles and also sulfuric acid induced soot activation in aircraft wakes, which initiate formation of contrails and perhaps even cirrus clouds

High-resolution land use and land cover dataset for regional climate modelling: Historical and future changes in Europe

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1&deg; resolution for Europe with annual LULC maps from 1950&ndash;2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by &nbsp;Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25&deg; resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.</p

MODELING THE TRANS-ATLANTIC TRANSPORTATION OF SAHARAN DUST

In the present study we are simulating the trans-Atlantic transport of dust from Sahara to the South-Central America, using the regional climate model RegCM4 and its online dust scheme, for the year 2007. The simulated horizontal and vertical distributions of the mineral dust optical properties were evaluated against the LIVAS CALIPSO satellite dust product. The Trans-Atlantic dust transport is simulated adequately with RegCM4, but there are some spatial discrepancies. Dust optical thickness is overestimated in the eastern Sahara throughout the year by 0.1-0.2, while near the gulf of Guinea is underestimated during winter and spring. Although RegCM4 dust plume is located southern on winter and spring, it doesn't spatially match the dust optical thickness of LIVAS. In summer and autumn the vertical distribution of dust between 3-4km during the Trans-Atlantic transport is simulated by the model adequately up to 30ºW 40ºW longitude. However, during winter-spring RegCM4 misplaces dust loading into higher altitude. Finally, we discuss some possible reasons and mechanisms that might be responsible for the differences between the model and the observations

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

International audienceAbstract. Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble

In the current work we present six hindcast WRF (Weather Research and Forecasting model) simulations for the EURO-CORDEX (European Coordinated Regional Climate Downscaling Experiment) domain with different configurations in microphysics, convection and radiation for the time period 1990?2008. All regional model simulations are forced by the ERA-Interim reanalysis and have the same spatial resolution (0.44°). These simulations are evaluated for surface temperature, precipitation, short- and longwave downward radiation at the surface and total cloud cover. The analysis of the WRF ensemble indicates systematic temperature and precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons. Overestimation of total cloud cover and underestimation of downward shortwave radiation at the surface, mostly linked to the Grell?Devenyi convection and CAM (Community Atmosphere Model) radiation schemes, intensifies the negative bias in summer temperatures over northern Europe (max ?2.5 °C). Conversely, a strong positive bias in downward shortwave radiation in summer over central (40?60%) and southern Europe mitigates the systematic cold bias over these regions, signifying a typical case of error compensation. Maximum winter cold biases are over northeastern Europe (?2.8 °C); this location suggests that land?atmosphere rather than cloud?radiation interactions are to blame. Precipitation is overestimated in summer by all model configurations, especially the higher quantiles which are associated with summertime deep cumulus convection. The largest precipitation biases are produced by the Kain?Fritsch convection scheme over the Mediterranean. Precipitation biases in winter are lower than those for summer in all model configurations (15?30%). The results of this study indicate the importance of evaluating not only the basic climatic parameters of interest for climate change applications (temperature and precipitation), but also other components of the energy and water cycle, in order to identify the sources of systematic biases, possible compensatory or masking mechanisms and suggest pathways for model improvement.The contribution from Universidad de Cantabria was funded by the Spanish R&D programme through projects CORWES (CGL2010-22158-C02-01) and WRF4G (CGL2011-28864), co-funded by the European Regional Development Fund. M. García-Díez acknowledges financial support from the EXTREMBLES (CGL2010-21869) project

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX Flagship Pilot Study Land Use and Climate Across Scales (LUCAS) models – Part 2: The role of changing vegetation

Land cover in sub-polar and alpine regions of northern and eastern Europe have already begun changing due to natural and anthropogenic changes such as afforestation. This will impact the regional climate and hydrology upon which societies in these regions are highly reliant. This study aims to identify the impacts of afforestation/reforestation (hereafter afforestation) on snow and the snow-albedo effect and highlight potential improvements for future model development. The study uses an ensemble of nine regional climate models for two different idealised experiments covering a 30-year period; one experiment replaces most land cover in Europe with forest, while the other experiment replaces all forested areas with grass. The ensemble consists of nine regional climate models composed of different combinations of five regional atmospheric models and six land surface models. Results show that afforestation reduces the snow-albedo sensitivity index and enhances snowmelt. While the direction of change is robustly modelled, there is still uncertainty in the magnitude of change. The greatest differences between models emerge in the snowmelt season. One regional climate model uses different land surface models which shows consistent changes between the three simulations during the accumulation period but differs in the snowmelt season. Together these results point to the need for further model development in representing both grass–snow and forest–snow interactions during the snowmelt season. Pathways to accomplishing this include (1) a more sophisticated representation of forest structure, (2) kilometre-scale simulations, and (3) more observational studies on vegetation–snow interactions in northern Europe

Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect

Seasonal snow cover plays a major role in the climate system of the Northern Hemisphere via its effect on land surface albedo and fluxes. In climate models the parameterization of interactions between snow and atmosphere remains a source of uncertainty and biases in the representation of local and global climate. Here, we evaluate the ability of an ensemble of regional climate models (RCMs) coupled with different land surface models to simulate snow–atmosphere interactions over Europe in winter and spring. We use a previously defined index, the snow-albedo sensitivity index (SASI), to quantify the radiative forcing associated with snow cover anomalies. By comparing RCM-derived SASI values with SASI calculated from reanalyses and satellite retrievals, we show that an accurate simulation of snow cover is essential for correctly reproducing the observed forcing over middle and high latitudes in Europe. The choice of parameterizations, and primarily the choice of the land surface model, strongly influences the representation of SASI as it affects the ability of climate models to simulate snow cover accurately. The degree of agreement between the datasets differs between the accumulation and ablation periods, with the latter one presenting the greatest challenge for the RCMs. Given the dominant role of land surface processes in the simulation of snow cover during the ablation period, the results suggest that, during this time period, the choice of the land surface model is more critical for the representation of SASI than the atmospheric model

The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part I: Evaluation of precipitation

Here we present the first multi-model ensemble of regional climate simulations at kilometer-scale horizontal grid spacing over a decade long period. A total of 23 simulations run with a horizontal grid spacing of ∼ 3 km, driven by ERA-Interim reanalysis, and performed by 22 European research groups are analysed. Six different regional climate models (RCMs) are represented in the ensemble. The simulations are compared against available high-resolution precipitation observations and coarse resolution (∼ 12 km) RCMs with parameterized convection. The model simulations and observations are compared with respect to mean precipitation, precipitation intensity and frequency, and heavy precipitation on daily and hourly timescales in different seasons. The results show that kilometer-scale models produce a more realistic representation of precipitation than the coarse resolution RCMs. The most significant improvements are found for heavy precipitation and precipitation frequency on both daily and hourly time scales in the summer season. In general, kilometer-scale models tend to produce more intense precipitation and reduced wet-hour frequency compared to coarse resolution models. On average, the multi-model mean shows a reduction of bias from ∼ −40% at 12 km to ∼ −3% at 3 km for heavy hourly precipitation in summer. Furthermore, the uncertainty ranges i.e. the variability between the models for wet hour frequency is reduced by half with the use of kilometer-scale models. Although differences between the model simulations at the kilometer-scale and observations still exist, it is evident that these simulations are superior to the coarse-resolution RCM simulations in the representing precipitation in the present-day climate, and thus offer a promising way forward for investigations of climate and climate change at local to regional scales.Fil: Ban, Nikolina. Universidad de Innsbruck; AustriaFil: Caillaud, Cécile. Université de Toulouse; FranciaFil: Coppola, Erika. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Pichelli, Emanuela. The Abdus Salam; Italia. The Abdus Salam. International Centre for Theoretical Physics; ItaliaFil: Sobolowski, Stefan. Norwegian Research Centre; NoruegaFil: Adinolfi, Marianna. Fondazione Centro Euro-Mediterraneo sui cambiamenti climatici; ItaliaFil: Ahrens, Bodo. Goethe Universitat Frankfurt; AlemaniaFil: Alias, Antoinette. Université de Toulouse; FranciaFil: Anders, Ivonne. German Climate Computing Center; AlemaniaFil: Bastin, Sophie. Universite Paris-Saclay;Fil: Belušić, Danijel. Swedish Meteorological and Hydrological Institute; SuizaFil: Berthou, Ségolène. Met Office Hadley Centre; Reino UnidoFil: Brisson, Erwan. Université de Toulouse; FranciaFil: Cardoso, Rita M.. Universidade Nova de Lisboa; PortugalFil: Chan, Steven C.. University of Newcastle; Reino UnidoFil: Christensen, Ole Bøssing. Danish Meteorological Institute; DinamarcaFil: Fernández, Jesús. Universidad de Cantabria; EspañaFil: Fita Borrell, Lluís. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Frisius, Thomas. Helmholtz Gemeinschaft; AlemaniaFil: Gaparac, Goran. Croatia Control Ltd.; CroaciaFil: Giorgi, Filippo. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; ItaliaFil: Goergen, Klaus. Centre for High-Performance Scientific Computing in Terrestrial Systems; Alemania. Helmholtz Gemeinschaft. Forschungszentrum Jülich; AlemaniaFil: Haugen, Jan Erik. Norwegian Meteorological Institute; NoruegaFil: Hodnebrog, Øivind. Center for International Climate and Environmental Research-Oslo; NoruegaFil: Kartsios, Stergios. Aristotle University Of Thessaloniki; GreciaFil: Katragkou, Eleni. Aristotle University Of Thessaloniki; GreciaFil: Kendon, Elizabeth J.. Met Office Hadley Centre; Reino UnidoFil: Keuler, Klaus. Brandenburg University of Technology Cottbus-Senftenberg; AlemaniaFil: Lavin Gullon, Alvaro. Universidad de Cantabria; EspañaFil: Lenderink, Geert. Royal Netherlands Meteorological Institute; Países Bajo

Is the ozone climate penalty robust in Europe?

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