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While the Intergovernmental Panel on Climate Change (IPCC) physical science reports usually assess a handful of future scenarios, the Working Group III contribution on climate mitigation to the IPCC's Sixth Assessment Report (AR6 WGIII) assesses hundreds to thousands of future emissions scenarios. A key task in WGIII is to assess the global mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emissions scenarios from different integrated assessment models (IAMs) come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth system models. In this work, we describe the “climate-assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1202 mitigation scenarios in AR6 WGIII. We evaluate the global mean temperature projections and effective radiative forcing (ERF) characteristics of climate emulators FaIRv1.6.2 and MAGICCv7.5.3 and use the CICERO simple climate model (CICERO-SCM) for sensitivity analysis. We discuss the implied overshoot severity of the mitigation pathways using overshoot degree years and look at emissions and temperature characteristics of scenarios compatible with one possible interpretation of the Paris Agreement. We find that the lowest class of emissions scenarios that limit global warming to “1.5 ∘C (with a probability of greater than 50 %) with no or limited overshoot” includes 97 scenarios for MAGICCv7.5.3 and 203 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 ∘C for up to a few decades before returning to below 1.5 ∘C by or before the year 2100. For more than half of the scenarios in this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, median temperatures decline by about 0.3–0.4 ∘C after peaking at 1.5–1.6 ∘C in 2035–2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss their implications. This article also introduces a “climate-assessment” Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work provides a community tool for assessing the temperature outcomes of emissions pathways and provides a basis for further work such as extending the workflow to include downscaling of climate characteristics to a regional level and calculating impacts.
Hot subdwarf B stars are core-helium-burning objects that have undergone envelope stripping, likely by a binary companion. Using high-speed photometry from the Transiting Exoplanet Survey Satellite, we have discovered the hot subdwarf BPM 36430 is a hybrid sdBV(rs) pulsator exhibiting several low-amplitude g-modes and a strong p-mode pulsation. The latter shows a clear, periodic variation in its pulse arrival times. Fits to this phase oscillation imply BPM 36430 orbits a barycenter approximately 10 light-seconds away once every 3.1 days. Using the CHIRON echelle spectrograph on the CTIO 1.5 m telescope, we confirm the reflex motion by detecting a radial-velocity variation with semiamplitude, period, and phase in agreement with the pulse timings. We conclude that a white dwarf companion with minimum mass of approximate to 0.42 M (circle dot) orbits BPM 36430. Our study represents only the second time a companion orbiting a pulsating hot subdwarf or white dwarf has been detected from pulse timings and confirmed with radial velocities.
The NGC 346 young stellar system and associated N66 giant H ii region in the Small Magellanic Cloud are the nearest example of a massive star-forming event in a low metallicity (Z approximate to 0.2Z (circle dot)) galaxy. With an age of less than or similar to 3 Myr this system provides a unique opportunity to study relationships between massive stars and their associated H ii region. Using archival data, we derive a total H alpha luminosity of L(H alpha) = 4.1 x 10(38) erg s(-1) corresponding to an H-photoionization rate of 3 x 10(50) s(-1). A comparison with a predicted stellar ionization rate derived from the more than 50 known O-stars in NGC 346, including massive stars recently classified from Hubble Space Telescope far-ultraviolet (FUV) spectra, indicates an approximate ionization balance. Spectra obtained with SALT suggest the ionization structure of N66 could be consistent with some leakage of ionizing photons. Due to the low metallicity, the FUV luminosity from NGC 346 is not confined to the interstellar cloud associated with N66. Ionization extends through much of the spatial extent of the N66 cloud complex, and most of the cloud mass is not ionized. The stellar mass estimated from nebular L(H alpha) appears to be lower than masses derived from the census of resolved stars which may indicate a disconnect between the formation of high and low mass stars in this region. We briefly discuss implications of the properties of N66 for studies of star formation and stellar feedback in low metallicity environments.
Beyond technology
(2022)
This article enriches the existing literature on the importance and role of the social sciences and humanities (SSH) in renewable energy sources research by providing a novel approach to instigating the future research agenda in this field. Employing a series of in-depth interviews, deliberative focus group workshops and a systematic horizon scanning process, which utilised the expert knowledge of 85 researchers from the field with diverse disciplinary backgrounds and expertise, the paper develops a set of 100 priority questions for future research within SSH scholarship on renewable energy sources. These questions were aggregated into four main directions: (i) deep transformations and connections to the broader economic system (i.e. radical ways of (re)arranging socio-technical, political and economic relations), (ii) cultural and geographical diversity (i.e. contextual cultural, historical, political and socio-economic factors influencing citizen support for energy transitions), (iii) complexifying energy governance (i.e. understanding energy systems from a systems dynamics perspective) and (iv) shifting from instrumental acceptance to value-based objectives (i.e. public support for energy transitions as a normative notion linked to trust-building and citizen engagement). While this agenda is not intended to be—and cannot be—exhaustive or exclusive, we argue that it advances the understanding of SSH research on renewable energy sources and may have important value in the prioritisation of SSH themes needed to enrich dialogues between policymakers, funding institutions and researchers. SSH scholarship should not be treated as instrumental to other research on renewable energy but as intrinsic and of the same hierarchical importance.
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.