What the EU/Singapore Digital Deal Means for Data Regulation – Technology Magazine
What the EU/Singapore Digital Deal Means for Data Regulation Technology Magazine
Month: July 2024
Rainfall projections under different climate scenarios over the Kaduna River Basin, Nigeria
Abstract
This research aimed to assess changes in mean and extreme rainfall within the Kaduna River Basin (KRB), specifically examining the implications of two Representative Concentration Pathways (RCPs)—4.5 and 8.5 scenarios. Employing a quantile mapping technique, this study corrected inherent biases in four Regional Climate Models, enabling the examination of mean precipitation and six indices capturing extreme precipitation events for the 2050s. These findings were compared against a historical reference period spanning from 1981 to 2010, considering the basin's upstream and downstream segments. Results revealed an average annual rainfall reduction under scenarios 4.5 (21.39%) and 8.5 (20.51%) across the basin. This decline exhibited a more pronounced impact on monthly rainfall during the wet season (April to October) compared to the dry season (November to March). Notably, a substantial decrement in wet indices, excluding consecutive wet days (CWD), was foreseen in both seasons for the upstream and downstream areas, signalling an impending drier climate. The anticipated rise in consecutive dry days (CDD) is poised to manifest prominently downstream attributed to global warming-induced climate change brought on by increased anthropogenic emissions of greenhouse gases. These findings accentuate a heterogeneous distribution of extreme rainfall, potentially leading to water scarcity issues throughout the KRB, especially impacting upstream users. Moreover, the projections hint at an increased risk of flash floods during intense wet periods. Consequently, this study advocates the implementation of targeted disaster risk management strategies within the KRB to address these foreseeable challenges.
On the suitability of a convolutional neural network based RCM-emulator for fine spatio-temporal precipitation
Abstract
High resolution regional climate models (RCM) are necessary to capture local precipitation but are too expensive to fully explore the uncertainties associated with future projections. To resolve the large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to learn the relationship between low-resolution predictors and high resolution surface variables. When trained the emulator can be applied to any low resolution simulation to produce ensembles of high resolution emulated simulations. This study assesses the suitability of applying the RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire precipitation distribution over any grid point. Under a perfect conditions framework, the resulting emulator shows striking ability to reproduce the RCM original series with an excellent spatio-temporal correlation. In particular, a very good behaviour is obtained for the two tails of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates consistent precipitation objects even if the highest frequency details are missed. The emulator quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces the correct high resolution spatial structure and heavy precipitation intensity. Nevertheless, further work is needed to establish a relevant evaluation framework for GCM applications.
Anxiety, depression, headaches — is political polarization bad for your health?
As growing evidence shows that engagement with politics is associated with worsening mental and physical health, a bumper election year and increasing polarization could affect millions.
Targeting net-zero emissions while advancing other sustainable development goals in China
Abstract
The global net-zero transition needed to combat climate change may have profound effects on the energy–food–water–air quality nexus. Accomplishing the net-zero target while addressing other environmental challenges to achieve sustainable development is a policy pursuit for all. Here we develop a multi-model interconnection assessment framework to explore and quantify the co-benefits and trade-offs of climate action for environment-related sustainable development goals in China. We find that China is making progress towards many of the sustainable development goals, but still insufficiently. The net-zero transition leads to substantial sustainability improvements, particularly in energy and water systems. However, the co-benefits alone cannot ensure a sustainable energy–food–water–air quality system. Moreover, uncoordinated policies may exacerbate threats to energy security and food security as variable renewables and bioenergy expand. We urge the implementation of pragmatic measures to increase incentives for demand management, improve food system efficiency, promote advanced irrigation technology and further strengthen air pollutant control measures.
Journalism and public trust in science
Abstract
Journalists are often the adult public’s central source of scientific information, which means that their reporting shapes the relationship the public has with science. Yet philosophers of science largely ignore journalistic communication in their inquiries about trust in science. This paper aims to help fill this gap in research by comparing journalistic norm conflicts that arose when reporting on COVID-19 and tobacco, among other policy-relevant scientific topics. I argue that the public’s image of scientists– as depositories of indisputable, value-free facts, trustworthy only when in consensus– makes it particularly difficult for journalists to ethically communicate policy-relevant science rife with disagreement. In doing so, I show how journalists, like scientists, face the problem of inductive risk in such cases. To overcome this problem, I sketch a model of trust in science that is grounded in an alternative image of scientists– what I call the responsiveness model of trust in science. By highlighting the process of science over its product, the responsiveness model requires scientists to respond to empirical evidence and the public’s values to warrant the public’s trust. I then show why this model requires journalists to be the public’s watchdogs by verifying and communicating whether scientists are being properly responsive both epistemically and non-epistemically.