Article
Green & Sustainable Science & Technology
Tom M. L. Wigley, Sanghyun Hong, Barry W. Brook
Summary: The study evaluated three Integrated Assessment Models (IAMs) and found discrepancies in their predictions related to historical data and future scenarios. It was noted that mitigation technology failures varied greatly among the IAMs, suggesting the need for a more thorough examination of their projections. Recommendations included improving the availability of comprehensive model output for further analysis, ensuring consistent comparative scenarios for assessment, and conducting detailed inter-model comparisons for deeper insights into model credibility.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Green & Sustainable Science & Technology
Camila Callegari, Tarik Tanure, Ana Carolina Oliveira Fiorini, Eduardo Haddad, Edson Domingues, Aline Magalhaes, Fernando Perobelli, Alexandre Porsse, Andre F. P. Lucena, Eveline Vasquez-Arroyo, Mariana Imperio, Luiz Bernardo Baptista, Roberto Schaeffer
Summary: Cities play a crucial role in addressing climate change and achieving sustainable development goals. To assist decision-makers in identifying sustainable solutions, a new methodology was developed that integrates assessment models with a database of available solutions. The results highlight the importance of new technologies, such as renewable energy, although their implementation requires institutional and financial support beyond the capacity of most municipalities. Nevertheless, Brazilian cities can still contribute to sustainability through regulation, financial incentives, and advocacy.
Article
Environmental Sciences
Haitao Yang, Tian Yang, Fan Yang, Xiao Yang
Summary: Seawater intrusion is a major threat to human health and economic development in coastal areas due to climate change. This study developed a coupled model, integrating SWAT-MODFLOW and SEAWAT, to predict seawater intrusion under different climate scenarios. The results showed that the model accurately reflected the flow and concentration distribution in the study area, and the uncertainty of seawater intrusion prediction was mainly derived from different global climate models.
ENVIRONMENTAL RESEARCH
(2023)
Editorial Material
Multidisciplinary Sciences
Laura Spinney
Summary: Studies have shown that infections like influenza and polio can have long-term effects on people even decades later, yet the long-term effects of COVID-19 are being underestimated.
Article
Environmental Sciences
Charlie Wilson, Celine Guivarch, Elmar Kriegler, Bas van Ruijven, Detlef P. van Vuuren, Volker Krey, Valeria Jana Schwanitz, Erica L. Thompson
Summary: This study provides a comprehensive synthesis of process-based IAM evaluation research, drawing on six different evaluation methods and proposing a systematic evaluation framework for informing climate policy.
Article
Meteorology & Atmospheric Sciences
Jiacheng Ye, Zhuo Wang
Summary: Many coupled climate models suffer from a late retreat bias in North American monsoon (NAM) simulations, which is manifested by overestimated precipitation in October. The overestimated precipitation has long been attributed to the negative sea surface temperature (SST) biases in the tropical Atlantic and insufficient model resolution to resolve mesoscale features. However, we found little correlation between CMIP6 model resolutions and the simulated NAM retreat-season precipitation in October. Instead, tropical eastern North Pacific SST biases and the associated large-scale circulation biases play a dominant role in inducing the retreat-season biases, with SST biases in other ocean basins playing a secondary role.
JOURNAL OF CLIMATE
(2023)
Article
Environmental Sciences
Salah U-Din, Mian Sajid Nazir, Muddassar Sarfraz
Summary: Climate warming in Canada is about double the magnitude of global warming, leading to increased impact of extreme weather events. This study finds that weather catastrophes have significant negative effects on the Canadian stock market, particularly in the IT and financial services sectors.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Environmental Sciences
Luksanaree Maneechot, Yong Jie Wong, Sophal Try, Yoshihisa Shimizu, Khagendra Pralhad Bharambe, Patinya Hanittinan, Teerawat Ram-Indra, Muhammad Usman
Summary: This study simulated extreme climatic flood events in the Chao Phraya River Basin, Thailand, under future climate simulation using post-processing techniques on the d4PDF model. The results showed that the proposed method could accurately assess climate change impacts, and emphasized the importance of proper post-processing techniques in improving the robustness of d4PDF.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Biodiversity Conservation
Kelley D. Erickson, Adam B. Smith
Summary: Species distribution models are useful for estimating the distribution and environmental preferences of rare species. However, sparse data makes it challenging to model these species. This study contrasts different modeling approaches and evaluates their accuracy based on sample size, niche breadth, and similarity to other species. The results indicate that the best model depends on the modeling goal, sample size, and niche characteristics.
Review
Environmental Sciences
Thi Lan Anh Dinh, Filipe Aires
Summary: Climate models are widely used in studying climate change impacts, but their direct use is often limited due to inherent limitations. Bias correction methods have been proposed to improve the simulations. This study presents an up-to-date review of these methods, comparing six representative quantile-based approaches for temperature and precipitation data in Europe. New diagnostic tools are recommended to measure the impact of the adjustment on the model's ability to reproduce observations and capture climate change signals.
Review
Health Care Sciences & Services
Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A. Pfeffer, Jason Fries, Nigam H. Shah
Summary: The success of foundation models like ChatGPT and AlphaFold has generated interest in developing similar models for electronic medical records (EMRs) to enhance patient care and hospital operations. However, there are critical gaps in our understanding of these models' capabilities amidst the recent hype. In this narrative review, we analyze 84 foundation models trained on non-imaging EMR data and establish a taxonomy outlining their architectures, training data, and potential applications. Most models are trained on limited clinical datasets or public biomedical corpora and are assessed on tasks that lack meaningful insights for health systems. Based on these findings, we propose an improved evaluation framework that focuses on healthcare-relevant metrics to measure the benefits of clinical foundation models.
NPJ DIGITAL MEDICINE
(2023)
Article
Green & Sustainable Science & Technology
Farzaneh Najimi, Babak Aminnejad, Vahid Nourani, Yong Zhang
Summary: This study assessed the impact of climate change on the mountainous watershed of the Jajrood River in Iran. The results showed a significant increase in future flow due to increased precipitation intensity and higher temperatures. However, despite the increased risk of flooding, climate change is intensifying the issue of water scarcity.
Article
Environmental Sciences
Isabel Fuentes-Santos, Uxio Labarta, Maria Jose Fernandez-Reiriz, Susan Kay, Solfrid Saetre Hjollo, X. Anton Alvarez-Salgado
Summary: The study suggests that the impact of climate change on mussel growth is relatively small compared to the importance of seeding time. The response of mussels to climate change varies across climate models, ranging from minor growth decline to moderate growth increase.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Meteorology & Atmospheric Sciences
Li-Wei Chao, Andrew E. Dessler
Summary: This study evaluates the performance of models from CMIP5 and CMIP6 by comparing feedbacks in models with those inferred from observations. It found no systematic disagreements between the two, but noted differences in individual models' ability to reproduce observations. The study also identified structural differences and unforced pattern effects as important sources of uncertainty in the model ensembles when comparing with observational data.
JOURNAL OF CLIMATE
(2021)
Article
Meteorology & Atmospheric Sciences
Ruyan Chen, Isla R. Simpson, Clara Deser, Bin Wang, Yan Du
Summary: This study explains why climate models tend to overestimate the springtime ENSO teleconnection to the North Pacific. The main cause behind this bias is the diabatic heating biases over the tropical Indian Ocean and central-western Pacific basins. The spring mean climate state is more sensitive to the biased heating than the winter mean state. These findings are useful for developing future climate models that would better simulate the springtime climate response during the ENSO events.
JOURNAL OF CLIMATE
(2022)