climate science
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A new multidisciplinary, international research program aims to tackle one of the grand challenges in climate science: resolving the Antarctic Ice Sheet’s contribution to future sea level rise.

While there is overwhelming scientific agreement on climate change, the public has become polarized over fundamental questions such as human-caused global warming. Communication strategies to reduce polarization rarely address the underlying cause: ideologically-driven misinformation. In order to effectively counter misinformation campaigns, scientists, communicators, and educators need to understand the arguments and techniques in climate science denial, as well as adopt evidence-based approaches to neutralizing misinforming content. This chapter reviews analyses of climate misinformation, outlining a range of denialist arguments and fallacies. Identifying and deconstructing these different types of arguments is necessary to design appropriate interventions that effectively neutralize the misinformation. This chapter also reviews research into how to counter misinformation using communication interventions such as inoculation, educational approaches such as misconception-based learning, and the interdisciplinary combination of technology and psychology known as technocognition.

Human consumption of livestock remains a marginal issue in climate change debates, partly due to the IPCC's arbitrary adoption of 100-year global warming potential framework to compare different emissions, blinding us to the significance of shorter-term emissions, namely methane. Together with the gas it reacts to form - tropospheric ozone - methane has been responsible for 37% of global warming since 1750, yet its atmospheric life is just 10 years. Neglecting its role means overlooking powerful mitigation opportunities. The chapter discusses the role of livestock, the largest anthropogenic methane source, and the need to include reduced meat consumption in climate change responses. Looking beyond the conventional focus on the consumer, we point to some underlying challenges in addressing the meat-climate relationship, including the climate science community's reluctance to adopt a short-term focus in its climate projections. Policy options are presented.

For many decades, vertical winds have been observed at high altitudes of the Earth’s atmosphere, in the mesosphere and thermosphere layers. These observations have been used with a simple one-dimensional model to make estimates of possible altitude climbs by biologically sized particles deeper into the thermosphere, in the rare occurrence where such a particle has been propelled to these altitudes. A particle transport mechanism is suggested from the literature on auroral arcs, indicating that an altitude of 120 km could be reached by a nanometre-sized particle, which is higher than the measured 77 km limit on the biosphere. Vertical wind observations in the upper mesophere and lower thermosphere are challenging to make and so we suggest that particles could reach altitudes greater than 120 km, depending on the magnitude of the vertical wind. Applications of the larger vertical winds in the upper atmosphere to astrobiology and climate science are explored.

Knowledge from indigenous scholars and communities plays a crucial role in managing global ecosystems and biodiversity conservation. As such, it is stipulated as Target 18 of the Aichi Biodiversity Targets.

Information flow provides a natural measure for the causal interaction between dynamical events. This study extends our previous rigorous formalism of componentwise information flow to the bulk information flow between two complex subsystems of a large-dimensional parental system. Analytical formulas have been obtained in a closed form. Under a Gaussian assumption, their maximum likelihood estimators have also been obtained. These formulas have been validated using different subsystems with preset relations, and they yield causalities just as expected. On the contrary, the commonly used proxies for the characterization of subsystems, such as averages and principal components, generally do not work correctly. This study can help diagnose the emergence of patterns in complex systems and is expected to have applications in many real world problems in different disciplines such as climate science, fluid dynamics, neuroscience, financial economics, etc.

This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics.