Search

The objective of this paper is to present a new data set of bias-corrected CMIP5 global climate models (GCMs) daily data over Africa. This dataset was obtained in using the Cumulative Distribution Function Transform (CDF-t) method, a method that has been applied on several regions and contexts but never on Africa. Here CDF-t is used over the period 1950–2099 combining historical runs and climate change scenarios on 6 variables, precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface down-welling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. Evaluation of the results is carried out over West Africa on a list of priority users-based metrics that was discussed and selected with stakeholders and on simulated yield using a crop model simulating maize growth. Bias-corrected GCMs data are compared with another available dataset of bias-corrected GCMs, and the impact of three different reference datasets on bias-corrections is also examined in details. CDF-t is very effective in removing the biases and in reducing the high inter-GCMs scattering. Differences with other bias-corrected GCMs data are mainly due to the differences between the reference datasets. This is particular true for surface down-welling shortwave radiation, which has impacts in terms of simulated maize yields. Projections of future yields over West Africa have quite different levels, depending on bias-correction method used, but they all show a similar relative decreasing trend over the 21st century. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs) the nodes can be either static or mobile depending on the requirements of each application. During the design of Medium Access Control (MAC) protocols, mobility may pose many communication challenges. These difficulties require first a link establishment between mobile and static nodes, and then an energy efficient and low delay burst handling mechanism. In this study, we investigate preamble-sampling solutions that allow asynchronous operation. We first introduce anycast transmission to ContikiMAC where a mobile node emits an anycast data packet whose first acknowledging node will serve as responsible to forward it towards the sink. Once this link is established, burst transmission can start, according to the respective burst handling mechanism of ContikiMAC. Although it is considered as negligible in the literature, such an anycast-based on-the-fly operation actually results in high packet duplication at the sink. Hence, we demonstrate that even a basic anycast-based M-ContikiMAC would fail to handle bursty traffic from mobile nodes mainly due to increased unnecessary traffic and channel occupancy. We then propose Mobility-Enhanced ContikiMAC (ME-ContikiMAC), a protocol that reduces packet duplications in the network by more than 90 % comparing to M-ContikiMAC. Moreover, our results in a 48-node scenario show that ME-ContikiMAC outperforms a number of state-of-the-art solutions (including MoX-MAC and MOBINET), by terms of reducing both delay and energy consumption. [ABSTRACT FROM AUTHOR]

Evaluating the potential climatic suitability for premium wine production is crucial for adaptation planning in Europe. While new wine regions may emerge out of the traditional boundaries, most of the present-day renowned winemaking regions may be threatened by climate change. Here, we analyse the future evolution of the geography of wine production over Europe, through the definition of a novel climatic suitability indicator, which is calculated over the projected grapevine phenological phases to account for their possible contractions under global warming. Our approach consists in coupling six different de-biased downscaled climate projections under two different scenarios of global warming with four phenological models for different grapevine varieties. The resulting suitability indicator is based on fuzzy logic and is calculated over three main components measuring (i) the timing of the fruit physiological maturity, (ii) the risk of water stress and (iii) the risk of pests and diseases. The results demonstrate that the level of global warming largely determines the distribution of future wine regions. For a global temperature increase limited to 2°C above the pre-industrial level, the suitable areas over the traditional regions are reduced by about 4%/°C rise, while for higher levels of global warming, the rate of this loss increases up to 17%/°C. This is compensated by a gradual emergence of new wine regions out of the traditional boundaries. Moreover, we show that reallocating better-suited grapevine varieties to warmer conditions may be a viable adaptation measure to cope with the projected suitability loss over the traditional regions. However, the effectiveness of this strategy appears to decrease as the level of global warming increases. Overall, these findings suggest the existence of a safe limit below 2°C of global warming for the European winemaking sector, while adaptation might become far more challenging beyond this threshold., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

This paper describes the extension of the previously CMIP5 based high-resolution climate projections with additional ones based on the more recent climate projections from the CMIP6 experiment. The downscaling method and data processing are the same but the reference dataset is now the ERA5-Land reanalysis (compared to ERA5 previously) allowing to increase the resolution of the new downscaled projections from 0.25° x 0.25° to 0.1°x 0.1°. The extension comprises 5 climate models and includes 2 surface variables at daily resolution: air temperature and precipitation. Three greenhouse gas emissions scenarios are available: Shared Socioeconomic Pathways with mitigation policy (SSP1-2.6), an intermediate one (SSP2-4.5), and one without mitigation (SSP5-8.5)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article., (© 2022 The Author(s).)

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article., (© 2021 The Authors.)