Timothy M. Lenton’s research while affiliated with University of Exeter and other places

Several general circulation models (GCMs) show bifurcations of their atmospheric state under a broad range of warm climates. These include some of the more extreme global warming scenarios. This bifurcation can cause the transition to a superrotating state, a state where its angular momentum exceeds the solid body rotation of the planet. Here we use an idealised GCM to simulate this transition by altering a single non-dimensional control parameter, the thermal Rossby number. For a bifurcation-induced transition there is potential for early warnings, and we look for these here. Typically used early warning indicators, variance and lag-1 autocorrelation, calculated for the mean zonal equatorial wind speed, increase and peak just before the transition. The full autocorrelation function taken at multiple lags is also oscillatory, with a period of 25 d preceding the transition. This oscillatory behaviour is reminiscent of a local supercritical Hopf bifurcation. Motivated by this extra structure, we use a generalised early warning vector technique based on principal oscillation patterns (POPs) to diagnose the dominant spatial modes of the horizontal wind field fluctuations. We find a zonal-wavenumber-0 pattern that we call the “precursor” mode that appears shortly before and disappears soon after the transition. We attribute the increase in the early warning indicators to this spatial precursor mode. This mode is correlated to oscillations in strength of the Hadley cells. Following the transition, an eastward-propagating zonal-wavenumber-1 mode of period ∼4 d dominates the dynamics. This mode appears to be representative of the Kelvin–Rossby instability others have previously identified. Although the control parameter used to simulate the transition is unlikely to be relevant to future climate change, the Kelvin–Rossby transition mechanism may well be relevant, and the simulations reported here do show early warnings and serve as a test bed for whether we can detect this transition before it happens.

Our aim in the present article is to communicate directly to researchers, policymakers, and the public. As scientists and academics, we feel it is our moral duty and that of our institutions to alert humanity to the growing threats that we face as clearly as possible and to show leadership in addressing them. In this report, we analyze the latest trends in a wide array of planetary vital signs. We also review notable recent climate-related disasters, spotlight important climate-related topics, and discuss needed policy interventions. This report is part of our series of concise annual updates on the state of the climate.

Through our three-way collaboration we sought to understand Gaia and its political implications from the bottom-up and from within. Here we introduce that view of Gaia and how the dialogue between a philosopher (Bruno), a scientist (Tim), and a historian and philosopher of science (Séb) turned into a research programme. This sets in context a previously unpublished piece by Latour: ‘There is nothing simple in a feedback loop – or why goal function is not the problem of Gaia’.

Precipitation variability is forecast to increase under climate change but its impacts on vegetation productivity are complex. Here, we use generalised additive models and remote sensing-derived datasets to quantify the effect of precipitation amount, distribution, and intensity on the gross primary productivity of dry rangelands across sub-Saharan Africa from 2000 to 2019 and differentiate these effects from other variables. We find that total precipitation is the primary driver of productivity, but that more variable rainfall has a small negative effect across vegetation types and rainfall regimes. Temperature and soil nitrogen also have strong effects, especially in drier rangelands. Shrublands and grasslands are more sensitive to environmental variability than savannas. Our findings support a model in which the main constraints on productivity are maintenance of soil moisture and minimisation of plant water stress. This highlights the risks of climate warming and increasing variability for productivity in water-limited grass and shrublands but suggests savannas may have greater resilience in Africa.

Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

The nature of the last universal common ancestor (LUCA), its age and its impact on the Earth system have been the subject of vigorous debate across diverse disciplines, often based on disparate data and methods. Age estimates for LUCA are usually based on the fossil record, varying with every reinterpretation. The nature of LUCA’s metabolism has proven equally contentious, with some attributing all core metabolisms to LUCA, whereas others reconstruct a simpler life form dependent on geochemistry. Here we infer that LUCA lived ~4.2 Ga (4.09–4.33 Ga) through divergence time analysis of pre-LUCA gene duplicates, calibrated using microbial fossils and isotope records under a new cross-bracing implementation. Phylogenetic reconciliation suggests that LUCA had a genome of at least 2.5 Mb (2.49–2.99 Mb), encoding around 2,600 proteins, comparable to modern prokaryotes. Our results suggest LUCA was a prokaryote-grade anaerobic acetogen that possessed an early immune system. Although LUCA is sometimes perceived as living in isolation, we infer LUCA to have been part of an established ecological system. The metabolism of LUCA would have provided a niche for other microbial community members and hydrogen recycling by atmospheric photochemistry could have supported a modestly productive early ecosystem.

Induced innovation is a multi-faceted process characterized by interaction between demand-pull forces, path-dependent self-reinforcing change, and the cost reduction of technology that occurs with cumulative deployment. By endogenously including induced innovation in energy models, policy analysts and modellers could enable a mission-oriented approach to policymaking that envisions the opportunities of accelerating the low-carbon energy transition while avoiding the risks of inaction. While the integrated assessment models used in the intergovernmental panel on climate change (IPCC-IAMs) account for induced innovation, their assumptions of general equilibrium and optimality may reveal weaknesses that produce unsatisfactory results for policymakers. In this paper, we develop a menu of options for modelling induced innovation in the energy transition with non-equilibrium, non-optimal models by a three step methodology: a modelling survey questionnaire, a review of the literature, and an analysis of case studies from modelling applications within the economics of energy innovation and system transition (EEIST) programme. The survey questionnaire allows us to compare 24 models from EEIST partner institutions developed to inform energy and decarbonisation policy decisions. We find that only six models, future technological transformations, green investment barriers mode, stochastic experience curves, economy-energy-environment macro-econometric, M3E3 and Dystopian Schumpeter meeting Keynes, represent endogenous innovation—in the form of learning curves, R&D, and spillover effects. The review of the literature and analysis of case studies allow us to form a typology of different models of induced innovation alongside the IPCC-IAMs and develop a decision tree to guide policy analysts and modellers in the choice of the most appropriate models to answer specific policy questions. The paper provides evidence for integrating narrow and systemic approaches to modelling-induced innovation in the context of low-carbon energy transition, and promotes cooperation instead of competition between different but complementary approaches. These findings are consistent with the implementation of risk-opportunity analysis as a policy appraisal method to evaluate low-carbon transition pathways.

Positive tipping points are promising leverage points in social systems for accelerated progress towards climate and sustainability targets. Besides their impact in specific social systems such as energy, food, or social norms and values, positive tipping dynamics may in some cases spread across different systems, amplifying the impact of tipping interventions. However, the cross-system interactions that can create such tipping cascades are sparsely examined. Here, we review interactions across sociotechnical, socioecological, socioeconomic, and sociopolitical systems that can lead to tipping cascades based on the emerging and relevant past evidence. We show that there are several feedback mechanisms where a strategic input can trigger secondary impacts for a disproportionately large positive response, and various agents that can trigger such cascades. This review of cross-system interactions facilitates the quantification and analysis of positive tipping cascades in future studies.

There is a diverging perception of climate tipping points, abrupt changes and surprises in the scientific community and the public. While such dynamics have been observed in the past, e.g., frequent reductions of the Atlantic meridional overturning circulation during the last ice age, or ice sheet collapses, tipping points might also be a possibility in an anthropogenically perturbed climate. In this context, high impact—low likelihood events, both in the physical realm as well as in ecosystems, will be potentially dangerous. Here we argue that a formalized assessment of the state of science is needed in order to establish a consensus on this issue and to reconcile diverging views. This has been the approach taken by the Intergovernmental Panel on Climate Change (IPCC). Since 1990, the IPCC has consistently generated robust consensus on several complex issues, ranging from the detection and attribution of climate change, the global carbon budget and climate sensitivity, to the projection of extreme events and their impact. Here, we suggest that a scientific assessment on tipping points, conducted collaboratively by the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, would represent an ambitious yet necessary goal to be accomplished within the next decade.