Aligning climate scenarios to emissions inventories shifts global benchmarks

Taking stock of global progress towards achieving the Paris Agreement requires consistently measuring aggregate national actions and pledges against modelled mitigation pathways1. However, national greenhouse gas inventories (NGHGIs) and scientific assessments of anthropogenic emissions follow different accounting conventions for land-based carbon fluxes resulting in a large difference in the present emission estimates2,3, a gap that will evolve over time. Using state-of-the-art methodologies4 and a land carbon-cycle emulator5, we align the Intergovernmental Panel on Climate Change (IPCC)-assessed mitigation pathways with the NGHGIs to make a comparison. We find that the key global mitigation benchmarks become harder to achieve when calculated using the NGHGI conventions, requiring both earlier net-zero CO2 timing and lower cumulative emissions. Furthermore, weakening natural carbon removal processes such as carbon fertilization can mask anthropogenic land-based removal efforts, with the result that land-based carbon fluxes in NGHGIs may ultimately become sources of emissions by 2100. Our results are important for the Global Stocktake6, suggesting that nations will need to increase the collective ambition of their climate targets to remain consistent with the global temperature goals.

Can updated climate pledges limit warming well below 2°C?

[Figure: see text].

pyam: Analysis and visualisation of integrated assessment and macro-energy scenarios

The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

Erratum: Publisher Correction: Current and future global climate impacts resulting from COVID-19

[This corrects the article DOI: 10.1038/s41558-020-0883-0.].

Erratum: Publisher Correction: Current and future global climate impacts resulting from COVID-19

[This corrects the article DOI: 10.1038/s41558-020-0883-0.].

Dynamic control of extracellular environment in in vitro neural recording systems

A technique is presented for rapid fabrication of microfluidic channels on top of multichannel in vitro neural recording electrode arrays. The channels allow dynamic control of both stable and transient flow patterns over localized areas of the array, over biologically relevant timescales. A cellular model consisting of thermally sensitive dorsal root ganglion neurons was integrated into the devices. The device was used to demonstrate precise control of the extracellular microenvironment of individual cells on the array. Since the methods presented here are not specific to a particular cell type or neural recording system, the technique is amenable to a wide range of applications within the neuroscience field.