Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations

In this work we asses the benefits of removing bias in climate forcing data used for hydrological climate change impact assessment at pan-European scale, with emphasis on floods. Climate simulations from the HIRHAM5-ECHAM5 model driven by the SRES-A1B emission scenario are corrected for bias using a histogram equalization method. As target for the bias correction we employ gridded interpolated observations of precipitation, average, minimum, and maximum temperature from the E-OBS data set. Bias removal transfer functions are derived for the control period 1961–1990. These are subsequently used to correct the climate simulations for the control period, and, under the assumption of a stationary error model, for the future time window 2071–2100. Validation against E-OBS climatology in the control period shows that the correction method performs successfully in removing bias in average and extreme statistics relevant for flood simulation over the majority of the European domain in all seasons. This translates into considerably improved simulations with the hydrological model of observed average and extreme river discharges at a majority of 554 validation river stations across Europe. Probabilities of extreme events derived employing extreme value techniques are also more closely reproduced. Results indicate that projections of future flood hazard in Europe based on uncorrected climate simulations, both in terms of their magnitude and recurrence interval, are likely subject to large errors. Notwithstanding the inherent limitations of the large-scale approach used herein, this study strongly advocates the removal of bias in climate simulations prior to their use in hydrological impact assessment

Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models

In the framework of the coordinated regional climate downscaling experiment (CORDEX), an ensemble of climate change projections for Africa has been created by downscaling the simulations of four global climate models (GCMs) by means of the consortium for small-scale modeling (COSMO) regional climate model (RCM) (COSMO-CLM, hereafter, CCLM). Differences between the projected temperature and precipitation simulated by CCLM and the driving GCMs are analyzed and discussed. The projected increase of seasonal temperature is found to be relatively similar between GCMs and RCM, although large differences (more than 1 °C) exist locally. Differences are also found for extreme-event related quantities, such as the spread of the upper end of the maximum temperature probability distribution function and, in turn, the duration of heat waves. Larger uncertainties are found in the future precipitation changes; this is partly a consequence of the inter-model (GCMs) variability over some areas (e.g. Sahel). However, over other regions (e.g. Central Africa) the rainfall trends simulated by CCLM and the GCMs show opposite signs, with CCLM showing a significant reduction in precipitation at the end of the century. This uncertain and sometimes contrasting behaviour is further investigated by analyzing the different models’ response to the land–atmosphere interaction and feedback. Given the large uncertainty associated with inter-model variability across GCMs and the reduced spread in the results when a single RCM is used for downscaling, we strongly emphasize the importance of exploiting fully the CORDEX-Africa multi-GCM/multi-RCM ensemble in order to assess the robustness of the climate change signal and, possibly, to identify and quantify the many sources of uncertainty that still remain

Nanoparticles obtained by confined impinging jet mixer: poly(lactide-co-glycolide) vs. Poly-ε-caprolactone

This paper is focused on the production and characterization of polymeric nanoparticles obtained by nanoprecipitation. The method consisted of using a confined impinging jet mixer (CIJM), circumventing high-energy equipment. Differences between the use of poly-ε-caprolactone (PCL) and poly(lactide-co-glycolide) (PLGA) as concerns particle mean size, zeta potential, and broad-spectrum antibiotic florfenicol entrapment were investigated. Other analyzed variables were polymer concentration, solvent, and anti-solvent flow rates, and antibiotic initial concentration. To our knowledge, no data were found related to PLGA and PCL nanoparticles comparison using CIJM. Also, florfenicol encapsulation within PCL or PLGA nanoparticles by nanoprecipitation has not been reported yet. The complexity of the nanoprecipitation phenomena has been confirmed, with many relevant variables involved in particles formation. PLGA resulted in smaller and more stable nanoparticles with higher entrapping of florfenicol than PCL.Fil: Turino, Ludmila Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Stella, Barbara. Università di Torino; ItaliaFil: Dosio, Franco. Università di Torino; ItaliaFil: Luna, Julio Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Barresi, Antonello A.. Politecnico di Torino; Itali

'Will the Paris Agreement protect us from hydro-meteorological extremes?'

Multi-hazard assessment is needed to understand compound risk. Yet, modelling of multiple climate hazards has been limitedly applied at the global scale to date. Here we provide a first comprehensive assessment of global population exposure to hydro-meteorological extremes—floods, drought and heatwaves—under different temperature increase targets. This study shows how limiting temperature increase to 1.5 and 2 °C, as for the goals of the Paris Agreement, could substantially decrease the share of global population exposed compared to a 3 °C scenario. In a 2 °C world, population exposure would drop by more than 50%, in Africa, Asia and the Americas, and by about 40% in Europe and Oceania. A 1.5 °C stabilization would further reduce exposure of about an additional 10% to 30% across the globe. As the Parties of the Paris Agreement are expected to communicate new or updated nationally determined contributions by 2020, our results powerfully indicate the benefits of ratcheting up both mitigation and adaptation ambition

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

We estimate the effects of climate anomalies (heat stress and drought) on annual maize production, variability, and trend from the country level to the global scale using a statistical model. Moderate climate anomalies and extremes are diagnosed with two indicators of heat stress and drought computed over maize growing regions during the most relevant period of maize growth. The calibrated model linearly combines these two indicators into a single Combined Stress Index. The Combined Stress Index explains 50% of the observed global production variability in the period 1980?2010. We apply the model on an ensemble of high-resolution global climate model simulations. Global maize losses, due to extreme climate events with 10-year return times during the period 1980?2010, will become the new normal already at 1.5 °C global warming levels (approximately 2020s). At 2 °C warming (late 2030s), maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions.Fil: Zampieri, M.. European Commission Joint Research Centre; ItaliaFil: Ceglar, A.. European Commission Joint Research Centre; ItaliaFil: Dentener, F.. European Commission Joint Research Centre; ItaliaFil: Dosio, A.. European Commission Joint Research Centre; ItaliaFil: Naumann, Gustavo. European Commission Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: van den Berg, M.. European Commission Joint Research Centre; ItaliaFil: Toreti, A.. European Commission Joint Research Centre; Itali

Improved Methods and Metrics for Assessing Impacts, Vulnerability and Adaptation

Over the course of the MEDIATION project, Work Package 2 was tasked with "develop[ing] and apply[ing] a toolbox, defined as a set of models, methods, and metrics for the assessment of impacts and vulnerability and adaptation options." As highlighted in Deliverable 2.2, many frameworks and methods for assessing adaptation have been developed over the last 20 years, yet these often have not been adopted in the context of formal adaptation policies in Europe and elsewhere. Reasons and problems include: (i) a fragmentation of methods and tools, (ii) a lack of linkages to actual policy needs, (iii) a lack of understanding and communication of uncertainties, (iv) the often expert-based nature and complexity of methods used versus actual user demands, and (v) a lack of consistent data, definitions and metrics. Deliverable 2.2 put forward a rough prototype for a toolbox of methods for studying impacts, vulnerability, and adaptation. In this deliverable, we discuss subsequent work on the MEDIATION toolbox, and report on application and testing of the improved methods and metrics in selected key European sectors and regions. We present feedback and improvement to methods and metrics based on input from case studies, stakeholders, and focus groups, as well as an overview of case study work and contribution to an improved MEDIATION toolbox. This input resulted in a number of conclusions relating to the development and use of methods and metrics, reducing uncertainty in CCIAV, and led to a number of changes, including the creation of a novel typology for classifying methods and models relating to CCIAV analysis. We provide an overview of the new typology, as well as the final toolbox, and summarize case study contributions towards improved methods and metrics

Adaptation to Increasing Risks of Forest Fires

This work presents a quantitative assessment of adaptation options in the context of forest fires in Europe under projected climate change. A standalone fire model (SFM) based on a state-of-the-art, large-scale forest fire modeling algorithm is used to explore fuel removal through prescribed burnings and improved fire suppression as adaptation options. The climate change projections are provided by three climate models reflecting the SRES A2 scenario. The SFM’s modeled burned areas for selected test countries in Europe show satisfying agreement with observed data coming from two different sources (European Forest Fire Information System and Global Fire Emissions Database). Our estimation of the potential increase in burned areas in Europe under ‘‘no adaptation’’ scenario is about 200% by 2090 (compared with 2000-2008). The application of prescribed burnings has the potential to keep that increase below 50%. Improvements in fire suppression might reduce this impact even further, for example, boosting the probability of putting out a fire within a day by 10% would result in about a 30% decrease in annual burned areas. By taking more adaptation options into consideration, such as using agricultural fields as fire breaks, behavioral changes, and long-term options, burned areas can be potentially reduced even further