Explore the vast range of titles available.

Carbon dioxide removal from the atmosphere (CDR)-also known as 'negative emissions'-features prominently in most 2 °C scenarios and has been under increased scrutiny by scientists, citizens, and policymakers. Critics argue that 'negative emission technologies' (NETs) are insufficiently mature to rely on them for climate stabilization. Some even argue that 2 °C is no longer feasible or might have unacceptable social and environmental costs. Nonetheless, the Paris Agreement endorsed an aspirational goal of limiting global warming to even lower levels, arguing that climate impacts-especially for vulnerable nations such as small island states-will be unacceptably severe in a 2 °C world. While there are few pathways to 2 °C that do not rely on negative emissions, 1.5 °C scenarios are barely conceivable without them. Building on previous assessments of NETs, we identify some urgent research needs to provide a more complete picture for reaching ambitious climate targets, and the role that NETs can play in reaching them.

Climate change might impact crop yields considerably and anticipated transformations of agricultural systems are needed in the coming decades to sustain affordable food provision. However, decision-making on transformational shifts in agricultural systems is plagued by uncertainties concerning the nature and geography of climate change, its impacts, and adequate responses. Locking agricultural systems into inadequate transformations costly to adjust is a significant risk and this acts as an incentive to delay action. It is crucial to gain insight into how much transformation is required from agricultural systems, how robust such strategies are, and how we can defuse the associated challenge for decision-making. While implementing a definition related to large changes in resource use into a global impact assessment modelling framework, we find transformational adaptations to be required of agricultural systems in most regions by 2050s in order to cope with climate change. However, these transformations widely differ across climate change scenarios: uncertainties in large-scale development of irrigation span in all continents from 2030s on, and affect two-thirds of regions by 2050s. Meanwhile, significant but uncertain reduction of major agricultural areas affects the Northern Hemisphere's temperate latitudes, while increases to non-agricultural zones could be large but uncertain in one-third of regions. To help reducing the associated challenge for decision-making, we propose a methodology exploring which, when, where and why transformations could be required and uncertain, by means of scenario analysis.

Operationalizing a Global Carbon Observing and Analysis System (www.geocarbon.net) would provide a sound basis for monitoring actual carbon fluxes and thus getting quantities right when pricing carbon - be it in a cap-and-trade scheme or under a tax regime. However, such monitoring systems are expensive and-especially in times of economic weakness-budgets for science and environmental policy are under particular scrutiny. In this study, we attempt to demonstrate the magnitude of benefits of improved information about actual carbon fluxes. Such information enables better-informed policy-making and thus paves the way for a more secure investment environment when decarbonizing the energy sector. The numerical results provide a robust indication of a positive social value of improving carbon monitoring systems when compared to their cost, especially for the more ambitious climate policies.

The AR6 Scenarios Database is a vital repository of climate change mitigation pathways used in the latest Intergovernmental Panel on Climate Change (IPCC) assessment cycle. In its current version, many scenarios in the database lack information about the level of anthropogenic carbon dioxide (CO2) removal via land sinks, as net-negative CO2 emissions and gross removals on land are not always separated and are not consistently reported across models. This makes scenario analyses focusing on CO2 removal challenging. We test and compare the performance of different regression models to impute missing data on land carbon sequestration for the global level and for several sub-global macro-regions from available data on net CO2 emissions from agriculture, forestry, and other land uses. We find that a k-nearest neighbors regression performs best among the tested regression models and use it to impute and provide two publicly available imputation datasets (https://doi.org/10.5281/zenodo.13373539, Prütz et al., 2024) on CO2 removal via land sinks for incomplete global scenarios (n=404) and incomplete regional R10 scenario variants (n=2358) of the AR6 Scenarios Database. We discuss the limitations of our approach, the use of our datasets for secondary assessments of AR6 scenario ensembles, and how this approach compares to other recent AR6 data reanalyses.

More chemicals can be smelled than there are olfactory receptors for them, necessitating a combinatorial representation by somewhat broadly tuned receptors. To understand the perception of odor quality and concentration, it is essential to establish the nature of the receptor repertoires that are activated by particular odorants at particular concentrations. We have taken advantage of the one-to-one correspondence of glomeruli and olfactory receptor molecules in the mouse olfactory bulb to analyze the tuning properties of a major receptor population by high resolution calcium imaging of odor responses selectively in the presynaptic compartment of glomeruli. We show that eighty different olfactory receptors projecting to the dorsal olfactory bulb respond to high concentrations of aldehydes with limited specificity. Varying ensembles of about 10 to 20 receptors encode any particular aldehyde at low stimulus concentrations with high specificity. Even normalized odor response patterns are markedly concentration dependent, caused by pronounced differences in affinity within the aldehyde receptor repertoire.

SILICON-BASED COMPUTERS WILL never be able to solve certain problems. This is true even if the number of transistors on a chip doubles every year or two for many years--with corresponding gains in performance. Merely combining these densely packed chips into multicore CPUs and stringing these CPUs into super-grid platforms does not change the fact that these processors work more or less serially on problems and many large optimization problems would take more than 10 billion years to solve serially, even on a computer that analyzed a million possible solutins per second.