Hydrosilicon

NCC papers on Event Attribution

Global Warming Has Accelerated: Are the United Nations and the Public Well-Informed? by Hansen et al 2025

Abstract
Global temperature leaped more than 0.4°C (0.7°F) during the past two years, the 12-month average peaking in August 2024 at +1.6°C relative to the temperature at the beginning of last century (the 1880-1920 average). This temperature jump was spurred by one of the periodic tropical El Niño warming events, but many Earth scientists were baffled by the magnitude of the global warming, which was twice as large as expected for the weak 2023-2024 El Niño. We find that most of the other half of the warming was caused by a restriction on aerosol emissions by ships, which was imposed in 2020 by the International Maritime Organization to combat the effect of aerosol pollutants on human health. Aerosols are small particles that serve as cloud formation nuclei. Their most important effect is to increase the extent and brightness of clouds, which reflect sunlight and have a cooling effect on Earth. When aerosols – and thus clouds – are reduced, Earth is darker and absorbs more sunlight, thus enhancing global warming. Ships are the main aerosol source in the North Pacific and North Atlantic Oceans. We quantify the aerosol effect from the geographical distribution of sunlight reflected by Earth as measured by satellites, with the largest expected and observed effects in the North Pacific and North Atlantic Oceans. We find that aerosol cooling, and thus climate sensitivity, are understated in the best estimate of the United Nations Intergovernmental Panel on Climate Change (IPCC).
Global warming caused by reduced ship aerosols will not go away as tropical climate moves into its cool La Niña phase. Therefore, we expect that global temperature will not fall much below +1.5°C level, instead oscillating near or above that level for the next few years, which will help confirm our interpretation of the sudden global warming. High sea surface temperatures and increasing ocean hotspots will continue, with harmful effects on coral reefs and other ocean life. The largest practical effect on humans today is increase of the frequency and severity of climate extremes. More powerful tropical storms, tornadoes, and thunderstorms, and thus more extreme floods, are driven by high sea surface temperature and a warmer atmosphere that holds more water vapor. Higher global temperature also increases the intensity of heat waves and – at the times and places of dry weather – high temperature increases drought intensity, including “flash droughts” that develop rapidly, even in regions with adequate average rainfall.
Polar climate change has the greatest long-term effect on humanity, with impacts accelerated by the jump in global temperature. We find that polar ice melt and freshwater injection onto the North Atlantic Ocean exceed prior estimates and, because of accelerated global warming, the melt will increase. As a result, shutdown of the Atlantic Meridional Overturning Circulation (AMOC) is likely within the next 20-30 years, unless actions are taken to reduce global warming – in contradiction to conclusions of IPCC. If AMOC is allowed to shut down, it will lock in major problems including sea level rise of several meters – thus, we describe AMOC shutdown as the “point of no return.”
We suggest that an alternative perspective – a complement to the IPCC approach – is needed to assess these issues and actions that are needed to avoid handing young people a dire situation that is out of their control. This alternative approach will make more use of ongoing observations to drive modeling and more use of paleoclimate to test modeling and test our understanding. As of today, the threats of AMOC shutdown and sea level rise are poorly understood, but better observations of polar ocean and ice changes in response to the present accelerated global warming have the potential to greatly improve our understanding.

Canada’s 10 most impactful weather stories of 2024

https://www.canada.ca/en/environment-climate-change/services/ten-most-impactful-weather-stories/2024.html

Climate Change in Practice Chapter 1

Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS- M6)

Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS- M6)
Stephen R. Sobie1 | Dhouha Ouali1 | Charles L. Curry1,2 | Francis W. Zwiers1,3
1Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia, Canada 2School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada 3Nanjing University of Information Science and Technology, Nanjing, China
This study presents a new dataset, Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS-M6), which provides statistically downscaled simulations of global climate models from the Sixth Coupled Model Intercomparison Project (CMIP6). The authors developed a new multivariate downscaling method called N-dimensional Multivariate Bias Correction (MBCn) to improve the representation of compound climate events, which involve interactions between multiple climate variables. This dataset uses PCIC-Blend, a new calibration dataset that combines existing gridded observational datasets to provide a more accurate representation of precipitation and temperature conditions across Canada. The authors evaluated the performance of MBCn compared to other downscaling methods and found that MBCn outperforms other methods, particularly in capturing dependencies between variables, which is essential for simulating compound climate events. The CanDCS-M6 dataset provides daily simulations of precipitation, maximum temperature, and minimum temperature at a high resolution for 26 global climate models, covering the historical period (1950–2014) and three future Shared Socioeconomic Pathways (SSPs) representing different future emissions scenarios. This dataset is intended to facilitate climate impact assessments, hydrologic modelling, and analysis tools for presenting climate projections for Canada.

Artificial Intelligence, Scientific Discovery, and Product Innovation

Artificial Intelligence, Scientific Discovery, and Product Innovation*
Aidan Toner-Rodgers†
MIT
November 6, 2024
This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product in-novation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complemen-tarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.

Scaling transformer neural networks for medium range weather forcasting

https://arxiv.org/abs/2312.03876 by Nguyen et al

AI-empowered next-generation multiscale climate modelling for mitigation and adaptation by Eyring et al. 2024

Summary
This research paper from Nature Geoscience proposes a novel approach to climate modelling that leverages artificial intelligence (AI) to enhance existing models. The authors argue that current Earth system models (ESMs) are limited by their coarse resolution and reliance on parameterizations for subgrid-scale processes, leading to systematic errors in climate projections. They propose a multiscale approach, which combines high-resolution climate models with improved coarser-resolution hybrid ESMs, which incorporate essential Earth system processes and feedbacks. This integration, they suggest, can be achieved by implementing machine learning (ML) algorithms to represent these subgrid-scale processes. The paper further calls for modernized infrastructures to support this approach, including increased use of Earth observations and operationalized policy-relevant simulations. This vision aims to create a step change in climate modelling, allowing for more accurate and timely projections to inform mitigation and adaptation strategies in a rapidly changing world.
https://www.nature.com/articles/s41561-024-01527-w

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