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This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

The edge of the RFX-mod (R=2m, a=0.46m) Reversed Field Pinch device is characterized by weak magnetic chaos affecting ion and electron diffusion. Edge particle transport is strongly influenced by a toroidal asymmetry caused by magnetic islands and an ambipolar radial electric field ensures local neutrality, in a way similar to the stochastic edge of tokamaks when resonant magnetic perturbations (RMPs) are applied. The H? emission and floating potential Vf measured in different poloidal and toroidal positions shows a helical shape of the Plasma Wall Interaction, fitting the spatial periodicity of the innermost resonant tearing mode (m/n=1/7) [1]. However, detailed measurements, along the poloidal (parallel) direction, of the electron density and temperature with the Thermal Helium Beam, and of the floating potential Vf with electrostatic probes, show that the response of the edge plasma depends on the poloidal angle, in a more complicated way than a pure 1/7 harmonic. In particular, multiple poloidal harmonics can be recognized in the measurements. The results are robust, because data analysis has been performed with different techniques: in terms of correlations between Vf signals and the corresponding local flux-surface displacement, by the conditional average technique applied at Vf signals, and finally also in terms of a travelling helical angle frame as reference of the measurements. The interpretation of the results is not obvious, but it highlights the fact that the correlation between magnetic islands and kinetic properties of the edge plasma is not a simple one-to-one causal relationship, as it is often assumed in RMP studies in tokamaks.

Diesel engine exhausts from a common rail 3.0 L F1C diesel engine were analyzed at two different load conditions of the WLTC testing cycle downstream of both the diesel particulate filter (DPF) and selective catalytic reactor (SCR) to verify their effect on the characteristics of carbon particulate matter. An array of chemical, physical and spectroscopic techniques (gas chromatography coupled with mass spectrometry (GC-MS), mobility analyzer, UV-Visible absorption and fluorescence spectroscopy) was applied for characterizing polycyclic aromatic hydrocarbons (PAH), heavy aromatic compounds and soot, constituting the particulate matter (PM) sampled from the exhaust. The engine was operated in half load (HL) (188 Nm, representing the more common condition for engine in urban traffic) and full load (FL) (452 Nm, representing the best performance of the engine operation) conditions, at the same engine speed (2000 rpm). Soot formation was enhanced in HL condition, with respect to FL, but, just because of the much lower soot amount, the after-treatment systems in this last condition resulted to be less efficient in the soot abatement. Indeed, the abatement through DPF was about 40% lower in the FL condition with respect to HL condition, and any significant further concentration decrease was found after SCR, in both conditions. By contrast, PAH concentration after DPF abatement was found to be higher in the HL with respect to FL condition. A further PAH concentration decrease of about 30% was found after the SCR in the HL condition whereas in FL the reduction was only about 5-6%. Also the heavy aromatic compounds having molecular weight above the GC-MS detection limit (300 u), were mitigated by SCR. Therefore, SCR did not cause a further soot reduction, whereas it was effective in largely reducing PAH and heavy aromatics emissions, especially in the lower temperature condition featuring the half-load condition, when combustion efficiency is worse. Moreover, SCR system reduced the emission of small particles probably due to an enhanced agglomeration of particles, with beneficial effect on the harmfulness to human health.

The severity of F. oxysporum f.sp. conglutinans on rocket plants grown under simulated climate change conditions has been studied. The rocket plants were cultivated on an infested substrate (4 log CFU g-1) and a non-infested substrate over three cycles. Pots were placed in six phytotrons in order to simulate different environmental conditions: 1) 400-450 ppm CO2, 18-22°C; 2) 800-850 ppm CO2, 18-22°C; 3) 400-450 ppm CO2, 22-26°C, 4) 800-850 ppm CO2, 22-26°C, 5) 400-450 ppm CO2, 26-30°C; 6) 800-850 ppm CO2, 26-30°C. Substrates from the infested and control samples were collected from each phytotron at 0, 60 and 120 days after transplanting. The disease index, microbial abundance, leaf physiological performances, root exudates and variability in the fungal profiles were monitored. The disease index was found to be significantly influenced by higher levels of temperature and CO2. Plate counts showed that fungal and bacterial development was not affected by the different CO2 and temperature levels, but a significant decreasing trend was observed from 0 up to 120 days. Conversely, the F. oxysporum f.sp. conglutinans plate counts did not show any significantly decrease from 0 up to 120 days. The fungal profiles, evaluated by means of polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE), showed a relationship to temperature and CO2 on fungal diversity profiles. Different exudation patterns were observed when the controls and infested plants were compared, and it was found that both CO2 and temperature can influence the release of compounds from the roots of rocket plants. In short, the results show that global climate changes could influence disease incidence, probably through plant-mediated effects, caused by soilborne pathogens.

While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.