It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Wednesday, December 24, 2025
UK’s worst-case climate risks laid bare for lawmakers
British policymakers planning for climate change now have detailed worst-case scenarios at their disposal, filling a gap that left the UK unprepared for extreme outcomes.
Scientists from the University of Reading have mapped out the most serious plausible climate threats facing the UK, including year-round cooling of up to 6°C if the Atlantic's currents collapse, temperature rises well above 4°C, or rapid sea level rises of over 2 metres by 2100.
The team's research, published today (Thursday, 18 December) in the journal Earth's Future, provides the practical tools that government guidance requires but that did not previously exist. Until now, sea level rise was the only area where extreme scenarios existed.
Professor Nigel Arnell, lead author of the study, said: "The climate extremes we have mapped aren't predictions, but they are plausible.
“The UK has been planning without the tools to test against worst-case scenarios. We've now given decision-makers what they need to prepare for climate outcomes they hope never happen, but can't afford to ignore."
Extreme changes mapped
The research team developed two sets of scenarios describing how climate could change in ways more severe than standard projections show. The scenarios describe physically plausible changes rather than likely outcomes. One set covers long-term changes up to 2100. The other describes extreme months and seasons that could occur at any time.
The six long-term scenarios show how climate could change more severely than conventional projections, and include:
Global temperatures rising well above 4°C by 2100
Rapid aerosol emission cuts causing up to 0.75°C additional warming
Major volcanic eruption causing 2.5°C cooling for five years
Enhanced Arctic warming reducing UK winter temperatures by 1.5°C by 2100
The short-term scenarios describe extreme individual months or seasons that could occur at any time. Hot months could see temperatures 4-6°C above average, while cold months could bring temperatures 4-7°C below average. Wet months could deliver rainfall 2.5-3 times the average, while dry months could see rainfall drop to just 10% of normal levels. Windy months could experience wind speeds 60-80% higher than average.
A new perspective emerged from a study published in the Journal of Management Science and Engineering: algorithmic control is not merely a "negative management tool". Instead, its three-dimensional functions (behavioral constraints, tracking evaluation, and standardized guidance) exert significantly different impacts on the turnover intention of gig workers, breaking the previous one-dimensional understanding of algorithmic control.
Simply put, the results show that behavioral constraints and tracking evaluation increase turnover intention by exacerbating relative deprivation, while standardized guidance mitigates this effect and even directly reduces turnover intention.
“Prior studies mostly focused on the overall impact of algorithmic control but ignored its functional heterogeneity,” says corresponding author Wei Cai. “Our study reveals the mediating role of ‘relative deprivation’ based on the JD-R model, and for the first time confirming that the standardized guidance inherent in algorithms can serve as a "buffering resource," providing a new approach for platforms to optimize management.”
The authors collected data from 242 food delivery riders through a two-stage questionnaire survey (one month apart) to minimize common method bias and enhance the persuasiveness of the conclusions.
“Algorithmic control does not only have negative impacts on gig workers – it can also trigger positive outcomes,” adds Cai. “This conclusion reminds platforms that there is no need to completely negate algorithmic management; instead, they can balance efficiency and humanistic care by strengthening standardized guidance (such as optimizing task matching and real-time feedback), thereby alleviating the industry pain point of high turnover rates.”
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Contact the author: Wei Cai, School of International Studies, Universiti Teknologi Malaysia, Johor Bahru, Malaysia, caiwei20211118@163.com
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
DOO (https://DeepOceanOmics.org/) integrates and analyzes multi-omics data from organisms in extreme marine environments in one platform, providing customized analytical tools to support cross-species comparative and evolutionary studies.
The Hong Kong University of Science and Technology (HKUST), in collaboration with the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), has launched the world's first Deep Ocean Omics (DOO) database (https://DeepOceanOmics.org/). As the largest platform of its kind, DOO integrates and analyzes multi-omics data from organisms thriving in the ocean's most extreme environments, alongside customized analytical tools to support cross-species comparative and evolutionary studies. By facilitating the utilization of deep-sea biological resources, the platform aims to advance scientific understanding of deep-sea biodiversity and ecosystems, and to foster global research and applications related to biological adaptation in extreme environments.
The deep ocean—defined as depths below 1000 meters— is one of Earth's largest yet least explored ecosystems. It hosts extraordinary biodiversity shaped by extreme conditions such as high pressure, oxygen deficiency, perpetual darkness, low temperatures, and limited nutrients. Although recent advances in high-throughput sequencing technologies have generated vast omics datasets from deep-sea species—revealing their unique adaptations in genetic, metabolic, and symbiotic mechanisms, the absence of unified resources, standardized data, and specialized analytical tools has hindered the consolidation and exploration of these multi-omics datasets.
To bridge this critical gap, a research team led by Prof. QIAN Peiyuan, Chair Professor of the Department of Ocean Science (OCES); Prof. WU Longjun, Assistant Professor of OCES; and Dr. SHE Jiajie, a postdoctoral researcher at HKUST, manually collected and integrated multi-omics data from 68 deep-sea animal species, including 72 genomes, 950 transcriptomes, 1,112 metagenomes, and 15 single-cell transcriptomes. Spanning seven phyla (Mollusca, Annelida, Arthropoda, Chordata, Cnidaria, Echinodermata, and Porifera), the database covers species from diverse deep-sea habitats such as cold seeps, hydrothermal vents, and seamounts. It also incorporates 1,413 fossil records to support evolutionary analysis of environmental adaptation strategies in deep-sea organisms. This makes DOO the most comprehensive multi-omics platform for deep-sea species in both taxonomic breadth and data dimensions.
The platform provides three specialized analytical modules:
Gene and Genome Module: For structural and functional annotation, transcription factors, ubiquitin families, transposons, gene family analysis and more.
Functional Genomics Analysis Module: Featuring gene co-expression networks, dynamic network views, single-cell data visualization, and metagenomic analysis.
Evolutionary and Comparative Genomics Module: Enabling pan-gene set analysis, micro- and macro-synteny analysis among deep-sea species, ancestral karyotype reconstruction, and phylogenetic tree construction.
All data on the platform can be visualized interactively through a customized genome browser, offering users an intuitive interface for integrated exploration.
DOO's focuses on three key research applications: decoding genetic information critical for survival in extreme environments, tracing evolutionary trajectories of deep-sea lineages, and studying host-microbe symbiosis essential for chemosynthetic ecosystems. As the first dedicated deep-sea multi-omics platform, it offers free access to curated datasets and tools for comparative genomics, benefiting researchers worldwide. Since its launch, DOO has attracted over 1,500 users from 40 countries.
Prof. Qian Peiyuan highlighted the platform’s scientific impact, stating, "By integrating multi-omics data, DOO enables systematic analysis of the adaptation mechanisms of deep‑sea organisms. It allows researchers to identify survival‑critical genes, reconstruct evolutionary histories spanning millions of years, and conduct large‑scale studies on chemosynthetic symbiosis. The consolidated data and tools provided by the database empower the global ocean science community to perform evolutionary and comparative genomic analyses more efficiently, thereby accelerating research into biodiversity and survival-adaptation strategies, and paving new pathways for humanity in addressing future challenges posed by extreme climatic environments."
The related paper, titled "DOO: Integrated Multi-Omics Resources for Deep Ocean Organisms," has been published in the prestigious journal Nucleic Acids Research. The study was co-corresponded by Prof. Qian Peiyuan and Prof. Wu Longjun, with Dr. She Jiajie as first author.
The project was supported by grants from the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), the Otto Poon Center for Climate Resilience and Sustainability, the Research Grants Council of Hong Kong, and the Science and Technology Innovation Committee of Shenzhen.
As the most comprehensive deep-sea multi-omics platform in terms of taxonomic breadth and data dimensions, DOO consolidates multi-omics data from 68 deep-sea animal species, covering 7 phyla: Mollusca, Annelida, Arthropoda, Chordata, Cnidaria, Echinodermata, and Porifera.
DOO features three specialized analytical modules, including the Gene and Genome Module, the Functional Genomics Analysis Module, and the Evolutionary and Comparative Genomics Module.
All data on the platform can be visualized interactively through a customized genome browser, offering users an intuitive interface for integrated exploration.
