Author: Latest Results

Comparison of conventional and machine learning methods for bias correcting CMIP6 rainfall and temperature in Nigeria

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Abstract

This research assesses the efficacy of thirteen bias correction methods, including traditional and machine learning-based approaches, in downscaling four chosen GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in Nigeria. The 0.5° resolution gridded rainfall, maximum temperature (Tmx), and minimum temperature (Tmn) of the Climate Research Unit (CRU) for the period 1975 − 2014 was used as the reference. The Compromise Programming Index (CPI) was used to assess the performance of bias correction methods based on three statistical metrics. The optimal bias-correction technique was employed to rectify bias to project the spatiotemporal variations in rainfall, Tmx, and Tmn over Nigeria for two distinct future timeframes: the near future (2021–2059) and the distant future (2060–2099). The study's findings indicate that the Random Forest (RF) machine learning technique better corrects the bias of all three climate variables for the chosen GCMs. The CPI of RF for rainfall, Tmx, and Tmn were 0.62, 0.0, and 0.0, followed by the Power Transformation approach with CPI of 0.74, 0.36, and 0.29, respectively. The geographic distribution of rainfall and temperatures significantly improved compared to the original GCMs using RF. The mean bias-corrected projections from the multimodel ensemble of the GCMs indicated a rainfall increase in the near future, particularly in the north by 2.7–12.7%, while a reduction in the south in the far future by -3.3% to -10% for different SSPs. The temperature projections indicated a rise in the Tmx and Tm from 0.71 °C and 0.63 °C for SSP126 to 2.71 °C and 3.13 °C for SSP585. This work highlights the significance of comparing bias correction approaches to determine the most suitable approach for adjusting biases in GCM estimations for climate change research.

Impact of population aging on future temperature-related mortality at different global warming levels

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Abstract

Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.

Assessment of the land use/land cover and climate change impact on the hydrological regime of the Kulsi River catchment, Northeast India

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Abstract

The present study has shown the changes in climatic variables and land use/land cover to observe the long-term changes in the hydrological characteristics of the Kulsi River. Mann–Kendall test, Expert Team on Climate Change Detection and Indices Index, t tests, and Analysis of variation were used to analyze the change and variation. The Mann–Kendall test shows a significant declining trend of rainfall at Chamaria (− 26.76 mm/year) annually, the seasonally significant declining trend was noticed in the winter season at the rate of (− 1.47 mm/year) at Ukium, whereas Chamaria showed a declining trend of rainfall in the post-monsoon season at the rate of (− 5.62 mm/year). Among all stations, maximum temperature showed a significant increasing trend at the rate of (0.03 °C/year) at Chaygaon, while minimum temperature showed a significant increasing trend at the rate of (0.02 °C/year) at Ukium and Chamaria, respectively. Consecutive dry days have shown significant positive trends at the rate of (0.85 days/year), (0.67 days/year) and (1.35 days/year) at Chaygaon, Ukium, and Chamaria. The areas under forest cover and water body have decreased, whereas cropland and transition areas have increased. In the case of the hydrological variables, only the water level has shown significant annual variation (p value is 0.002). However, both water level and discharge show seasonal variation significantly. The water level has shown a significant negative relation with maximum temperature (R and p values are − 0.78 and 0.00 respectively) and a significant positive relation with forest cover (R and p values are 0.6 and 0.01 respectively). On the other hand, water discharge has a significant positive relation with annual rainfall (R and p values are 0.5 and 0.03, respectively). The findings of the research show that increasing maximum temperature and decreasing forest cover are responsible for the decline of water level in the Kulsi River. Whereas, a decrease in rainfall leads to a decrease in discharge and vice versa. The findings of the present research will be helpful to the policymakers and government in preparing a suitable sustainable river management plan to address the declining water resources to mitigate water scarcity issues.

Compounding effects of changing sea level and rainfall regimes on pluvial flooding in New York City

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Abstract

Coastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC’s assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC’s drainage models focusing on rainfall-induced flooding. We showed that the city’s stormwater system is highly vulnerable to high-intensity short-duration “cloudburst” events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC’s residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city.

Towards an Epistemology of ‘Speciesist Ignorance’

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Abstract

The literature on the epistemology of ignorance already discusses how certain forms of discrimination, such as racism and sexism, are perpetuated by the ignorance of individuals and groups. However, little attention has been given to how speciesism—a form of discrimination on the basis of species membership—is sustained through ignorance. Of the few animal ethicists who explicitly discuss ignorance, none have related this concept to speciesism as a form of discrimination. However, it is crucial to explore this connection, I argue, as ignorance is both an integral part of the injustice done to animals as well as an obstacle to improving their treatment. In order to adequately criticize sustained structural speciesism and injustices towards animals, I develop an epistemological account of ‘speciesist ignorance’. I begin by defining and distinguishing between individual and group-based accounts of speciesist ignorance. I argue that humans, taken as a group, enjoy a position of privilege, which allows them to comfortably remain ignorant of their participation in collective wrongdoings towards animals. Additionally, I point out that speciesist ignorance is structurally encouraged and thereby maintains the dominant view that the human-animal-relationship, as it stands, is just. In sum, this article lays the groundwork for a social epistemology of speciesist ignorance. In particular, it informs further debate about individual and institutional epistemic duties to inquire into speciesism and to inform the public, about the moral culpability of ignorant actions, and about effective animal advocacy and policy which actively rejects speciesist ignorance.

The potential of generative AI for personalized persuasion at scale

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Abstract

Matching the language or content of a message to the psychological profile of its recipient (known as “personalized persuasion”) is widely considered to be one of the most effective messaging strategies. We demonstrate that the rapid advances in large language models (LLMs), like ChatGPT, could accelerate this influence by making personalized persuasion scalable. Across four studies (consisting of seven sub-studies; total N = 1788), we show that personalized messages crafted by ChatGPT exhibit significantly more influence than non-personalized messages. This was true across different domains of persuasion (e.g., marketing of consumer products, political appeals for climate action), psychological profiles (e.g., personality traits, political ideology, moral foundations), and when only providing the LLM with a single, short prompt naming or describing the targeted psychological dimension. Thus, our findings are among the first to demonstrate the potential for LLMs to automate, and thereby scale, the use of personalized persuasion in ways that enhance its effectiveness and efficiency. We discuss the implications for researchers, practitioners, and the general public.

The animal agriculture industry, US universities, and the obstruction of climate understanding and policy

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Abstract

The 2006 United Nations report “Livestock’s Long Shadow” provided the first global estimate of the livestock sector’s contribution to anthropogenic climate change and warned of dire environmental consequences if business as usual continued. In the subsequent 17 years, numerous studies have attributed significant climate change impacts to livestock. In the USA, one of the largest consumers and producers of meat and dairy products, livestock greenhouse gas emissions remain effectively unregulated. What might explain this? Similar to fossil fuel companies, US animal agriculture companies responded to evidence that their products cause climate change by minimizing their role in the climate crisis and shaping policymaking in their favor. Here, we show that the industry has done so with the help of university experts. The beef industry awarded funding to Dr. Frank Mitloehner from the University of California, Davis, to assess “Livestock’s Long Shadow,” and his work was used to claim that cows should not be blamed for climate change. The animal agriculture industry is now involved in multiple multi-million-dollar efforts with universities to obstruct unfavorable policies as well as influence climate change policy and discourse. Here, we traced how these efforts have downplayed the livestock sector’s contributions to the climate crisis, minimized the need for emission regulations and other policies aimed at internalizing the costs of the industry’s emissions, and promoted industry-led climate “solutions” that maintain production. We studied this phenomenon by examining the origins, funding sources, activities, and political significance of two prominent academic centers, the CLEAR Center at UC Davis, established in 2018, and AgNext at Colorado State University, established in 2020, as well as the influence and industry ties of the programs’ directors, Dr. Mitloehner and Dr. Kimberly Stackhouse-Lawson. We developed 20 questions to evaluate the nature, extent, and societal impacts of the relationship between individual researchers and industry groups. Using publicly available evidence, we documented how the ties between these professors, centers, and the animal agriculture industry have helped maintain the livestock industry’s social license to operate not only by generating industry-supported research, but also by supporting public relations and policy advocacy.

Academic capture in the Anthropocene: a framework to assess climate action in higher education

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Abstract

Higher education institutions have a mandate to serve the public good, yet in many cases fail to adequately respond to the global climate crisis. The inability of academic institutions to commit to purposeful climate action through targeted research, education, outreach, and policy is due in large part to “capture” by special interests. Capture involves powerful minority interests that exert influence and derive benefits at the expense of a larger group or purpose. This paper makes a conceptual contribution to advance a framework of “academic capture” applied to the climate crisis in higher education institutions. Academic capture is the result of the three contributing factors of increasing financialization issues, influence of the fossil fuel industry, and reticence of university employees to challenge the status quo. The framework guides an empirical assessment evaluating eight activities and related indices of transparency and participation based on principles of climate justice and the growing democracy-climate nexus. The framework can be a helpful tool for citizens and academics to assess the potential for academic capture and capacity for more just and democratic methods of climate action in higher education. We conclude with a series of recommendations on how to refine and apply our framework and assessment in academic settings. Our goal is to further the discussion on academic capture and continue to develop tools that transform higher education institutions to places of deep democracy and innovative climate education, research, and outreach to meet the challenges of the Anthropocene.

Environmental epistemology

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Abstract

We argue that there is a large class of questions—specifically questions about how to epistemically evaluate environments—that currently available epistemic theories are not well-suited for answering, precisely because these questions are not about the epistemic state of particular agents or groups. For example, if we critique Facebook for being conducive to the spread of misinformation, then we are not thereby critiquing Facebook for being irrational, or lacking knowledge, or failing to testify truthfully. Instead, we are saying something about the social media environment. In this paper, we first propose that a new branch of epistemology–Environmental Epistemology–is needed to address these questions. We argue that environments can be proper objects of epistemic evaluation, and that there are genuine epistemic norms that govern environments. We then provide a positive account of these norms and conclude by considering how recognition of these norms may require us to rethink longstanding epistemic debates.

Wisdom in the Age of AI Education

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Abstract

The disruptive potential of artificial intelligence (AI) augurs a requisite evolutionary concern for artificial wisdom (AW). However, given both a dearth of institutionalized scientific impetus and culturally subjective understandings of wisdom, there is currently no consensus surrounding its future development. This article provides a succinct overview of wisdom within various cultural traditions to establish a foundational common ground for both its necessity and global functioning in the age of AI. This is followed by a more directed argument in favor of pedagogical practices that inculcate students with a theoretical/practical wisdom in support of individual/collective critical capacities directed at democratic planetary stewardship in the age of AI education. The article concludes with a distilled synthesis of wisdom philosophies as principles that establish a framework for the development of a new planetary ethics built upon a symbiotic relationship between humans-technology and nature.