One-pot tandem catalytic epoxidation – CO2 insertion of monounsaturated methyl oleate to the corresponding cyclic organic carbonate

Conversion of unsaturated fatty acids, FAMEs or triglycerides into the corresponding cyclic organic carbonates involves two reaction steps-double-bond epoxidation and CO2 insertion into the epoxide-that are generally conducted separately. We describe an assisted-tandem catalytic protocol able to carry out carbonation of unsaturated methyl oleate in one-pot without isolating the epoxide intermediate. Methyl oleate carbonate was obtained in 99% yield and high retention of cis-configuration starting from methyl oleate using hydrogen peroxide and CO2 as green reagents, in a biphasic system and in the presence of an ammonium tungstate ionic liquid catalyst with KBr as co-catalyst

Reaction of Glycerol with Trimethyl Orthoformate: Towards the Synthesis of New Glycerol Derivatives

The reactivity of glycerol with trimethyl orthoformate is here described with an emphasis on developing a reliable synthetic approach for glycerol valorization. The glycerol based orthoester 4-(dimethoxymethoxy)methyl)-2-methoxy-1,3-dioxolane (3) was synthesized, under catalytic as well as catalyst-free conditions, by taking advantage of the thermodynamically controlled equilibrium between intermediates. Both Brønsted and Lewis acid catalysts accelerated the attainment of such an equilibrium, particularly Brønsted acidic ionic liquids BSMImHSO4 and BSMImBr were the most effective compounds for this reaction. The kinetic profiles allowed the proposal of a mechanism that accounts for the selectivity of the reaction

Representation of spatial and temporal variability of daily wind speed and of intense wind events over the Mediterranean Sea using dynamical downscaling: impact of the regional climate model configuration

Atmospheric datasets coming from long term reanalyzes of low spatial resolution are used for different purposes. Wind over the sea is, for example, a major ingredient of oceanic simulations. However, the shortcomings of those datasets prevent them from being used without an adequate corrective preliminary treatment. Using a regional climate model (RCM) to perform a dynamical downscaling of those large scale reanalyzes is one of the methods used in order to produce fields that realistically reproduce atmospheric chronology and where those shortcomings are corrected. Here we assess the influence of the configuration of the RCM used in this framework on the representation of wind speed spatial and temporal variability and intense wind events on a daily timescale. Our RCM is ALADIN-Climate, the reanalysis is ERA-40, and the studied area is the Mediterranean Sea. <br><br> First, the dynamical downscaling significantly reduces the underestimation of daily wind speed, in average by 9 % over the whole Mediterranean. This underestimation has been corrected both globally and locally, and for the whole wind speed spectrum. The correction is the strongest for periods and regions of strong winds. The representation of spatial variability has also been significantly improved. On the other hand, the temporal correlation between the downscaled field and the observations decreases all the more that one moves eastwards, i.e. further from the atmospheric flux entry. Nonetheless, it remains ~0.7, the downscaled dataset reproduces therefore satisfactorily the real chronology. <br><br> Second, the influence of the choice of the RCM configuration has an influence one order of magnitude smaller than the improvement induced by the initial downscaling. The use of spectral nudging or of a smaller domain helps to improve the realism of the temporal chronology. Increasing the resolution very locally (both spatially and temporally) improves the representation of spatial variability, in particular in regions strongly influenced by the complex surrounding orography. The impact of the interactive air-sea coupling is negligible for the temporal scales examined here. Using two different forcing datasets induces differences on the downscaled fields that are directly related to the differences between those datasets. Our results also show that improving the physics of our RCM is still necessary to increase the realism of our simulations. Finally, the choice of the optimal configuration depends on the scientific objectives of the study for which those wind datasets are used

An innovative approach for detecting the effect of climate change on the hydrometeorological extremes

In a future climate, extreme hydrometeorological events are expected to increase in magnitude and frequency. However, changes in the extreme event characteristics on a relatively short time-scale could be attributed to either climate fluctuations or the effect of anthropogenic climate change. How to distinguish between these two cases is still a field of research. This study presents a novel technique to detect systematic changes in the hydrometeorological extremes in Africa, as part of the eXtreme Climate Facilities project (XCF) lead by the African Risk Capacity (ARC). In a first step, we introduce the Extreme Climate Index (ECI), an objective, multi-hazard index constructed to identify intense droughts, storms, and heat weaves. Subsequently, a new method that estimates the probability of anthropogenic climate change to be the cause of the changes in the hydrometeorological extremes is introduced. This technique is applied to the case of XCF, which is aimed at designing a new financial tool to mitigate the anthropogenic effect on extremes. The method is calibrated with synthetic datasets as well as with the results of the pre-industrial experiment of the CMIP5 database. At the same time, this analysis explores the extent to which such a technique is generally applicable to the identification of systematic changes in the hydrometeorological extremes

Distance sampling effectively monitored a declining population of Italian roe deer Capreolus capreolus italicus

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Droughts Prediction: a Methodology Based on Climate Seasonal Forecasts

This study proposes a methodology for the drought assessment based on the seasonal forecasts. These are climate predictions of atmospheric variables, such as precipitation, temperature, wind speed, for upcoming season, up to 7\ua0months. In regions particularly vulnerable to droughts and to changes in climate, such as the Mediterranean areas, predictions of precipitation with months in advance are crucial for understanding the possible shifts, for example, in water resource availability. Over Europe, practical applications of seasonal forecasts are still rare, because of the uncertainties of their skills; however, the predictability varies depending on the season and area of application. In this study, we describe a methodology which integrates, through a statistical approach, seasonal forecast and reanalysis data to assess the climate state, i.e. drought or not, of a region for predefined periods in the next future, at monthly scale. Additionally, the skill of the forecasts and the reliability of the released climate state assessment are estimated in terms of the false rate, i.e. the probability of missing alerts or false alarms. The methodology has been first built for a case study in Zakynthos (Greece) and then validated for a case study in Sicily (Italy). The selected locations represent two areas of the Mediterranean region often suffering from drought and water shortage situations. Results showed promising findings, with satisfying matching between predictions and observations, and false rates ranging from 1 to 50%, depending on the selected forecast period

North Atlantic MOC variability and the Mediterranean Outflow: A box-model study

A simple box-model is used to investigate the effect of intermediate level heat/freshwater fluxes on the variability of the oceanic meridional overturning circulation. The model includes a simple representation of the spreading of the Mediterranean Outflow Water in the North Atlantic. We identify an internal advective feedback affecting the amplitude of the thermohaline oscillations. When a salinity gradient is maintained in the ocean interior the oscillations are amplified. Instead, if the intermediate level fluxes are spread in the ocean deep layers, the model variability is reduced. We suggest that this mechanism may be relevant for climate variability on interdecadal timescales

Thermohaline circulation sensitivity to intermediate‐level anomalies

A two-dimensional Boussinesq ocean model has been used to investigate the effect ofintermediate-level thermal and saline anomalies on the known multiple equilibria structure ofthe thermohaline circulation. These anomalies are taken as a crude representation of theMediterranean outflow in the Atlantic Ocean. The associated perturbation drives the systemtowards an overturning which resembles the present average Atlantic thermohaline circulation.The sensitivity to the depth at which the anomaly is placed is also investigated. We found thatnear-surface anomalies are more efficient in affecting the structure of the equilibria.DOI: 10.1034/j.1600-0870.2002.01284.

A multi-model climate response over tropical Africa at +2 °C

The impact of a +2 °C global warming on temperature and precipitation over tropical Africa is examined, based on an ensemble of 12 regional climate model scenario simulations. These 12 scenarios are re-phased so that they all correspond to the same global warming of 2 °C with respect to pre-industrial conditions. The continental temperature increase is above the global average. If heat waves are defined with the same temperature threshold in the reference climate and in the scenario, their frequency increases by a factor of 10. When the temperature threshold is adapted to future conditions, there is still a slight increase in frequency. The average precipitation does not show a significant response, due to model-to-model spread. However two compensating phenomena occur, which are robust among the models: (a) the number of rain days decreases whereas the precipitation intensity increases, and (b) the rain season occurs later during the year with less precipitation in early summer and more precipitation in late summer. Simulated daily temperature and precipitation data are combined in two impact models, one for the hydrology of the Nile and Niger basins, one for the food security of the different countries. They show that the main feature of the climate change is not a continuous trend signal, but an alternation of dry and wet decadal to multidecadal episodes

Benefits from using combined dynamical-statistical downscaling approaches - Lessons from a case study in the Mediterranean region

Abstract. Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two downscaling approaches: the deterministic dynamical downscaling (DD) and the statistical downscaling (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953–2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches