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During the Last Glacial Maximum (LGM), atmospheric CO2 was around 90 ppmv lower than during the pre-industrial period. The reasons for this decrease are most often elucidated through factorial experiments testing the impact of individual mechanisms. Due to uncertainty in our understanding of the real system, however, the different models used to conduct the experiments inevitably take on different parameter values and different structures. In this paper, the objective is therefore to take an uncertainty-based approach to investigating the LGM CO2 drop by simulating it with a large ensemble of parameter sets, designed to allow for a wide range of large-scale feedback response strengths. Our aim is not to definitely explain the causes of the CO2 drop but rather explore the range of possible responses. We find that the LGM CO2 decrease tends to predominantly be associated with decreasing sea surface temperatures (SSTs), increasing sea ice area, a weakening of the Atlantic Meridional Overturning Circulation (AMOC), a strengthening of the Antarctic Bottom Water (AABW) cell in the Atlantic Ocean, a decreasing ocean biological productivity, an increasing CaCO3 weathering flux and an increasing deep-sea CaCO3 burial flux. The majority of our simulations also predict an increase in terrestrial carbon, coupled with a decrease in ocean and increase in lithospheric carbon. We attribute the increase in terrestrial carbon to a slower soil respiration rate, as well as the preservation rather than destruction of carbon by the LGM ice sheets. An initial comparison of these dominant changes with observations and paleoproxies other than carbon isotope and oxygen data (not evaluated directly in this study) suggests broad agreement. However, we advise more detailed comparisons in the future, and also note that, conceptually at least, our results can only be reconciled with carbon isotope and oxygen data if additional processes not included in our model are brought into play.

Predictions for sea-level rise from Antarctica this century range from zero to over one metre. The highest are driven by the controversial ‘marine ice cliff instability’ (MICI) hypothesis, where coastal ice cliffs rapidly collapse after ice shelves disintegrate from surface and sub-shelf melting caused by global warming. Here we quantify ice sheet modelling uncertainties for the MICI study and show the probability distributions are skewed towards lower values (most likely value: 45 cm under very high greenhouse gas concentrations). However, MICI is not required to reproduce sea-level changes in the mid-Pliocene, Last Interglacial or 1992-2017, and without it the results agree with previous studies (all 95th percentiles are less than 43 cm). We therefore find previous interpretations of the MICI projections over-estimate sea-level rise this century. The hypothesis is still poorly-constrained: confidence in projections with MICI would require much greater diversity in models of ice shelf vulnerability and ice cliff collapse.

Climate model components involve both high-dimensional input and output fields. It is desirable to e ciently generate spatio-temporal out-puts of these models for applications in integrated assessment modelling or to assess the statistical relationship between such sets of inputs and outputs, for example, uncertainty analysis. However, the need for e ciency often compromises the fidelity of output through the use of low complexity models. Here, we develop a technique which combines statistical emulation with a dimensionality reduction technique to emulate a wide range of outputs from an atmospheric general circulation model, PLASIM, as functions of the boundary forcing prescribed by the ocean component of a lower complexity climate model, GENIE-1. Although accurate and detailed spatial information on atmospheric variables such as precipitation and wind speed is well beyond the capability of GENIE-1’s energy-moisture balance model of the atmosphere, this study demonstrates that the output of this model is useful in predicting PLASIM’s spatio-temporal fields through multi-level emulation. Meaningful information from the fast model, GENIE-1 was extracted by utilising the correlation between variables of the same type in the two models and between variables of di↵erent types in PLASIM. We present here the construction and validation of several PLASIM variable emulators and discuss their potential use in developing a hybrid model with statistical components.

The research described in this paper is undertaken under the banner of the smart city, a concept that captures the way urban spaces are re-made by the incursion of new technology. Much of smart is centred on converting everyday activities into data, and using this data to generate knowledge mediated by technology. Ordinary citizens, those that may have their lives impacted by the technology, usually are not properly involved in the ‘smartification’ process. Their perceptions, concerns and expectations should inform the conception and development of smart technologies at the same extent. How to engage general public with smart cities research is the central challenge for the Making Metrics Meaningful (MMM) project. Applying a rapid participatory method, ‘Imagine’ over a five-month period (March – July) the research sought to gain insights from the general public into novel forms of information system innovation. This brief paper describes the nature of the accelerated research undertaken and explores some of the themes which emerged in the analysis. Generic themes, beyond the remit of an explicit transport focus, are developed and pointers towards further research directions are discussed. Participatory methods, including engaging with self- selected transport users actively through both picture creation and programmatically specific musical ‘signatures’ as well as group discussion, were found to be effective in eliciting users’ own concerns, needs and ideas for novel information systems.

To study climate change on multi-millennial timescales or to explore a model’s parameter space, efficient models with simplified and parameterised processes are required. However, the reduction in explicitly modelled processes can lead to underestimation of some atmospheric responses that are essential to the understanding of the climate system. While more complex general circulations are available and capable of simulating a more realistic climate, they are too computationally intensive for these purposes. In this work, we propose a multi-level Gaussian emulation technique to efficiently estimate the outputs of steady-state simulations of an expensive atmospheric model in response to changes in boundary forcing. The link between a computationally expensive atmospheric model, PLASIM (Planet Simulator), and a cheaper model, EMBM (energy–moisture balance model), is established through the common boundary condition specified by an ocean model, allowing for information to be propagated from one to the other. This technique allows PLASIM emulators to be built at a low cost. The method is first demonstrated by emulating a scalar summary quantity, the global mean surface air temperature. It is then employed to emulate the dimensionally reduced 2-D surface air temperature field. Even though the two atmospheric models chosen are structurally unrelated, Gaussian process emulators of PLASIM atmospheric variables are successfully constructed using EMBM as a fast approximation. With the extra information gained from the cheap model, the multi-level emulator of PLASIM’s 2-D surface air temperature field is built using only one-third the amount of expensive data required by the normal single-level technique. The constructed emulator is shown to capture 93.2% of the variance across the validation ensemble, with the averaged RMSE of 1.33 °C. Using the method proposed, quantities from PLASIM can be constructed and used to study the effects introduced by PLASIM’s atmosphere.

This article proposes a fundamental methodological shift in the modelling of policy interventions for sustainability transitions in order to account for complexity (e.g. self-reinforcing mechanisms, such as technology lock-ins, arising from multi-agent interactions) and agent heterogeneity (e.g. differences in consumer and investment behaviour arising from income stratification). We first characterise the uncertainty faced by climate policy-makers and its implications for investment decision-makers. We then identify five shortcomings in the equilibrium and optimisation-based approaches most frequently used to inform sustainability policy: (i) their normative, optimisation-based nature, (ii) their unrealistic reliance on the full-rationality of agents, (iii) their inability to account for mutual influences among agents (multi-agent interactions) and capture related self-reinforcing (positive feedback) processes, (iv) their inability to represent multiple solutions and path-dependency, and (v) their inability to properly account for agent heterogeneity. The aim of this article is to introduce an alternative modelling approach based on complexity dynamics and agent heterogeneity, and explore its use in four key areas of sustainability policy, namely (1) technology adoption and diffusion, (2) macroeconomic impacts of low-carbon policies, (3) interactions between the socio-economic system and the natural environment, and (4) the anticipation of policy outcomes. The practical relevance of the proposed methodology is subsequently discussed by reference to four specific applications relating to each of the above areas: the diffusion of transport technology, the impact of low-carbon investment on income and employment, the management of cascading uncertainties, and the cross-sectoral impact of biofuels policies. In conclusion, the article calls for a fundamental methodological shift aligning the modelling of the socio-economic system with that of the climatic system, for a c

Agricultural productivity, and its variation in space and time, plays a fundamental role in many theories of human social evolution. However, we often lack systematic information about the productivity of past agricultural systems on a scale large enough to test these theories properly. The effect of climate on crop yields has received a great deal of attention resulting in a range of empirical and process-based models, yet the focus has primarily been on current or future conditions. In this paper, we argue for a “bottom-up” approach that estimates potential productivity based on information about the agricultural practices and technologies used in past societies. Of key theoretical interest is using this information to estimate the carrying high quality historical and archaeological information about past societies in order to infer the temporal and geographic patterns of change in agricultural productivity and potential. We discuss information we need to collect about past agricultural techniques and practices, and introduce a new databank initiative that we have developed for collating the best available historical and archaeological evidence. A key benefit of our approach lies in making explicit the steps in the estimation of past productivities and carrying capacities, and in being able to assess the effects of different modelling assumptions. This is undoubtedly an ambitious task, yet promises to provide important insights into fundamental aspects of past societies, enabling us to test more rigorously key hypotheses about human socio-cultural evolution.

This paper develops a modelling framework that links GEMINI-E3, a multi-regional, multi-sectoral computable general equilibrium model with a cost-benefit analysis approach at local level using geographical information system tools to assess the physical and economic consequences of sea-level rise (SLR) in the twenty first century. A set of future scenarios is developed spanning the uncertainties related to global warming, the parameters of semi-empirical SLR estimates, and coastal developments (cropland, urban areas and population). The importance of incorporating uncertainties regarding coastal development is highlighted. The simulation results suggest that the potential development of future coastal areas is a greater source of uncertainty than the parameters of SLR itself in terms of the economic consequences of SLR. At global level, the economic impact of SLR could be significant when loss of productive land along with loss of capital and forced displacement of populations are considered. Furthermore, highly urbanised and densely populated coastal areas of South East Asia, Australia and New Zealand are likely to suffer significantly if no protective measures are taken. Hence, it is suggested that coastal areas needs to be protected to ameliorate the overall welfare cost across various regions.

Equilibrium climate sensitivity measures the long-term response of surface temperature to changes in atmospheric CO2. The range of climate sensitivities in the IPCC AR5 Report is unchanged from that published almost 30 years earlier in the Charney Report. We conduct perfect-model experiments using an energy balance model to study the rate at which uncertainties might be reduced by observation of global temperature and ocean heat uptake. We find that a climate sensitivity of 1.5 <°C can be statistically distinguished from 3 °C by 2030; 3 °C from 4.5 °C by 2040; and 4.5 °C from 6 °C by 2065. Learning rates are slowest in the scenarios of greatest concern (high sensitivities), due to a longer ocean response time, which may have bearing on wait-and-see vs. precautionary mitigation policies. Learning rates are optimistic in presuming the availability of whole- ocean heat data, but pessimistic by using simple aggregated metrics and model physics.

Much research is still needed to understand the climate vulnerability of the energy sector and to identify cost-effective adaptation options. This chapter explores the coupling of the World TIMES Integrated Assessment Model (TIAM-WORLD) with an emulated version of the climate model PLASIM-ENTS to as- sess the impacts of future temperature and precipitation changes on the heating and cooling subsector and available hydropower. An absence of climate feedback induced by the adaptation of the energy system to future heating and cooling needs was found for a 1.6-5.7 K range of long-term global mean temperature increase: when aggregated at the global level, some changes compensate others, and heating and cooling represent a relatively small contributor to total energy consumption. However, significant changes are observed at the regional level in terms of additional power capacity, mostly coal power plants, to satisfy the additional cooling needs. Reduced needs for heating affect gas and coal heating systems more than biomass and electric heaters, reflecting higher costs of these heating options in the longer term. Available hydropower is estimated to increase on a seasonal basis in most regions under future climate change. It could therefore con- tribute to supply the additional electricity needed for cooling in regions where both future cooling needs and hydropower potential are expected to increase. Hydropower results are however characterized by high uncertainty due to uncertain- ties in projected precipitation changes as well as the relatively coarse resolution of PLASIM-ENTS.

Running complex computer models can be expensive in computer time, while learning about the relationships between input and output variables can be difficult. An emulator is a fast approximation to a computationally expensive model that can be used as a surrogate for the model, to quantify uncertainty or to improve process understanding. Here, we examine emulators based on singular value decompositions and use them to emulate global climate and vegetation fields, examining how these fields are affected by changes in the Earth’s orbit. The vegetation field may be emulated directly from the orbital variables, but an appealing alternative is to relate it to emulations of the climate fields, which involves high-dimensional input and output. The singular value decompositions radically reduce the dimensionality of the input and output spaces and are shown to clarify the relationships between them. The method could potentially be useful for any complex process with correlated, high- dimensional inputs and/or outputs.

Whether the widely accepted 2 °C limit for climate change is practically achievable depends partly on climate sensitivity, but predominantly on complex socio-economic dynamics.

The potential effects of climate change on the environment and society are many. In order to effectively quantify the uncertainty associated with these effects, highly complex simulation models are run with detailed representations of ecosystem processes. These models are computationally expensive and can involve a computer run of several days. Computationally cheaper models can be obtained from large ensembles of simulations using statistical emulation. The purpose of this paper is to construct a cheaper computational model (emulator) from simulations of the Lund- Potsdam-Jena managed Land (LPJmL), which is a dynamic global vegetation and crop model. This paper focuses on statistical emulation of potential crop yields from LPJmL and an emulator is constructed using a combination of ordinary least squares, principal component analysis and weighted least squares methods. For five climate models, under cross-validation the percentage of variance explained ranges from 60- 88% for the rainfed crops and 62-93% for the irrigated crops. The emulator can be used to predict potential crop yield change under any future climate scenarios and management options.

The energy sector is not only a major contributor to greenhouse gases, it is also vulnerable to climate change and will have to adapt to future climate conditions. The objective of this study is to analyze the impacts of changes in future temperatures on the heating and cooling services of buildings and the resulting energy and macro-economic effects at global and regional levels. For this purpose, the techno-economic TIAM-WORLD (TIMES Integrated Assessment Model) and the general equilibrium GEMINI-E3 (General Equilibrium Model of International-National Interactions between Economy, Energy and Environment) models are coupled with a climate model, PLASIM-ENTS (Planet-Simulator - Efficient Numerical Terrestrial Scheme). The key results are as follows. At the global level, the climate feedback induced by adaptation of the energy system to heating and cooling is found to be insignificant, partly because heating and cooling-induced changes compensate and partly because they represent a limited share of total final energy consumption. However, significant changes are observed at regional levels, more particularly in terms of addi- tional power capacity required to satisfy additional cooling services, resulting in increases in electricity prices. In terms of macro-economic impacts, welfare gains and losses are associated more with changes in energy exports and imports than with changes in energy consumption for heating and cooling. The rebound effect appears to be non-negligible. To conclude, the coupling of models of different nature was successful and showed that the energy and economic impacts of climate change on heating and cooling remain small at the global level, but changes in energy needs will be visible at more local scale.

This paper summarizes the results of an intercomparison project with Earth System Models of Intermediate Complexity (EMICs) undertaken in support of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The focus is on long-term climate projections designed to: (i) quantify the climate change commitment of different radiative forcing trajectories, and (ii) explore the extent to which climate change is reversible on human timescales. All commitment simulations follow the four Representative Concentration Pathways (RCPs) and their extensions to 2300. Most EMICs simulate substantial surface air temperature and thermosteric sea level rise commitment following stabilization of the atmospheric composition at year-2300 levels. The meridional overturning circulation (MOC) is weakened temporarily and recovers to near pre-industrial values in most models for RCPs 2.6–6.0. The MOC weakening is more persistent for RCP 8.5. Elimination of anthropogenic CO2 emissions after 2300 results in slowly decreasing atmospheric CO2 concentrations. At year 3000 atmospheric CO2 is still at more than half its year-2300 level in all EMICs for RCPs 4.5–8.5. Surface air temperature remains constant or decreases slightly and thermosteric sea level rise continues for centuries after elimination of CO2 emissions in all EMICs. Restoration of atmospheric CO2 from RCP to pre-industrial levels over 100–1000 years requires large artificial removal of CO2 from the atmosphere and does not result in the simultaneous return to pre-industrial climate conditions, as surface air temperature and sea level response exhibit a substantial time lag relative to atmospheric CO2.

Paleoclimate evidence and climate models indicate that certain elements of the climate system may exhibit thresholds, with small changes in greenhouse gas emissions resulting in non-linear and potentially irreversible regime shifts with serious consequences for socio-economic systems. Such thresholds or tipping points in the climate system are likely to depend on both the magnitude and rate of change of surface warming. The collapse of the Atlantic thermohaline circulation (THC) is one example of such a threshold. To evaluate mitigation policies that curb greenhouse gas emissions to levels that prevent such a climate threshold being reached, we use the MERGE model of Manne, Mendelsohn and Richels. Depending on assumptions on climate sensitivity and technological progress, our analysis shows that preserving the THC may require a fast and strong greenhouse gas emission reduction from today's level, with transition to nuclear and/or renewable energy, possibly combined with the use of carbon capture and sequestration systems.

Meeting the net-zero carbon emissions commitments of major economies by mid-century requires large-scale deployment of negative emission technologies (NETs). Terrestrial enhanced rock weathering on croplands (ERW) is a NET with co-benefits for agriculture, soils and ocean acidification that creates opportunities for generating income unaffected by diminishing carbon taxes as emissions approach net-zero. Here we show that ERW deployment with croplands to deliver net 2 Gt CO2 yr−1 removal approximately doubles the probability of meeting the Paris 1.5 °C target at 2100 from 23% to 42% in a high mitigation Representative Concentration Pathway 2.6 baseline climate. Carbon removal via carbon capture and storage (CCS) at the same rate had an equivalent effect. Co-deployment of ERW and CCS tripled the chances of meeting a 1.5 °C target (from 23% to 67%), and may be sufficient to reverse about one third of the surface ocean acidification effect caused by increases in atmospheric CO2 over the past 200 years. ERW increased the percentage of coral reefs above an aragonite saturation threshold of 3.5 from 16% to 39% at 2100, higher than CCS, highlighting a co-benefit for marine calcifying ecosystems. However, the degree of ocean state recovery in our simulations is highly uncertain and ERW deployment cannot substitute for near-term rapid CO2 emissions reductions.

Homo sapiens is the only species alive able to take advantage of its cognitive abilities to inhabit almost all environments on Earth. Humans are able to culturally construct, rather than biologically inherit, their occupied climatic niche to a degree unparalleled within the animal kingdom. Precisely when hominins acquired such an ability remains unknown, and scholars disagree on the extent to which our ancestors shared this same ability. Here, we settle this issue using fine-grained palaeoclimatic data, extensive archaeological data and phylogenetic comparative methods. Our results indicate that whereas early hominins were forced to live under physiologically suitable climatic conditions, with the emergence of H. heidelbergensis, the Homo climatic niche expanded beyond its natural limits, despite progressive harshening in global climates. This indicates that technological innovations providing effective exploitation of cold and seasonal habitats predated the emergence of Homo sapiens.

The newly developed fast Earth system model CLIMBER-X is presented. The climate component of CLIMBER-X consists of a 2.5-D semi-empirical statistical–dynamical atmosphere model, a 3-D frictional–geostrophic ocean model, a dynamic–thermodynamic sea ice model and a land surface model. All the model components are discretized on a regular lat–long grid with a horizontal resolution of 5°x5°. The model has a throughput of ∼ 10 000 simulation years per day on a single node with 16 CPUs on a high-performance computer and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to >100 000 years. A comprehensive evaluation of the model performance for the present day and the historical period shows that CLIMBER-X is capable of realistically reproducing many observed climate characteristics, with results that generally lie within the range of state-of-the-art general circulation models. The analysis of model performance is complemented by a thorough assessment of climate feedbacks and model sensitivities to changes in external forcings and boundary conditions. Limitations and applicability of the model are critically discussed. CLIMBER-X also includes a detailed representation of the global carbon cycle and is coupled to an ice sheet model, which will be described in separate papers. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate.

The distribution of ownership of transition risk associated with stranded fossil-fuel assets remains poorly understood. We calculate that global stranded assets as present value of future lost profits in the upstream oil and gas sector exceed US\$1 trillion under plausible changes in expectations about the effects of climate policy. We trace the equity risk ownership from 43,439 oil and gas production assets through a global equity network of 1.8 million companies to their ultimate owners. Most of the market risk falls on private investors, overwhelmingly in OECD countries, including substantial exposure through pension funds and financial markets. The ownership distribution reveals an international net transfer of more than 15% of global stranded asset risk to OECD-based investors. Rich country stakeholders therefore have a major stake in how the transition in oil and gas production is managed, as ongoing supporters of the fossil-fuel economy and potentially exposed owners of stranded assets.

The predicted Antarctic contribution to global‐mean sea‐level rise is one of the most uncertain among all major sources. Partly this is because of instability mechanisms of the ice flow over deep basins. Errors in bedrock topography can substantially impact the projected resilience of glaciers against such instabilities. Here we analyze the Pine Island Glacier topography to derive a statistical model representation. Our model allows for inhomogeneous and spatially dependent uncertainties and avoids unnecessary smoothing from spatial averaging or interpolation. A set of topography realizations is generated representing our best estimate of the topographic uncertainty in ice sheet model simulations. The bedrock uncertainty alone creates a 5%–25% uncertainty in the predicted sea level rise contribution at year 2100, depending on friction law and climate forcing. Pine Island Glacier simulations on this new set are consistent with simulations on the BedMachine reference topography but diverge from Bedmap2 simulations.

During the last glacial–interglacial cycle, Arctic biotas experienced substantial climatic changes, yet the nature, extent and rate of their responses are not fully understood1–8. Here we report a large-scale environmental DNA metagenomic study of ancient plant and mammal communities, analysing 535 permafrost and lake sediment samples from across the Arctic spanning the past 50,000 years. Furthermore, we present 1,541 contemporary plant genome assemblies that were generated as reference sequences. Our study provides several insights into the long-term dynamics of the Arctic biota at the circumpolar and regional scales. Our key findings include: (1) a relatively homogeneous steppe–tundra flora dominated the Arctic during the Last Glacial Maximum, followed by regional divergence of vegetation during the Holocene epoch; (2) certain grazing animals consistently co-occurred in space and time; (3) humans appear to have been a minor factor in driving animal distributions; (4) higher effective precipitation, as well as an increase in the proportion of wetland plants, show negative effects on animal diversity; (5) the persistence of the steppe–tundra vegetation in northern Siberia enabled the late survival of several now-extinct megafauna species, including the woolly mammoth until 3.9 ± 0.2 thousand years ago (ka) and the woolly rhinoceros until 9.8 ± 0.2 ka; and (6) phylogenetic analysis of mammoth environmental DNA reveals a previously unsampled mitochondrial lineage. Our findings highlight the power of ancient environmental metagenomics analyses to advance understanding of population histories and long-term ecological dynamics.

The benefits of implementing negative emission technologies in the global warming response to cumulative carbon emissions until the year 2420 are assessed following the shared socioeconomic pathway (SSP) 1-2.6, the sustainable development scenario, with a comprehensive set of intermediate-complexity Earth system model integrations. Model integrations include 86 different model realisations covering a wide range of plausible climate states. The global warming response is assessed in terms of two key climate metrics: the effective transient climate response to cumulative CO2 emissions (eTCRE), measuring the surface warming response to cumulative carbon emissions and associated non-CO2 forcing, and the effective zero emissions commitment (eZEC), measuring the extent of any continued warming after net-zero CO2 emissions are reached. The transient climate response to cumulative CO2 emissions (TCRE) is estimated as 2.2 K EgC−1 (median value) with a 10 %–90 % range of 1.75 to 3.13 K EgC−1 in 2100, approximated from the eTCRE by removing the contribution of non-CO2 forcing. During the positive emission phase, the eTCRE decreases from 2.71 (2.0 to 3.65) to 2.61 (1.91 to 3.62) K EgC−1 due to a weakening in the dependence of radiative forcing on atmospheric carbon, which is partly opposed by an increasing fraction of the radiative forcing warming the surface as the ocean stratifies. During the net negative and zero emission phases, a progressive reduction in the eTCRE to 2.0 (1.39 to 2.96) K EgC−1 is driven by the reducing airborne fraction as atmospheric CO2 is drawn down mainly by the ocean. The model uncertainty in the slopes of warming versus cumulative CO2 emissions varies from being controlled by the radiative feedback parameter during positive emissions to being affected by carbon-cycle parameters during net negative emissions, consistent with the drivers of unc

Enhanced Rock Weathering is a proposed Carbon Dioxide Removal technology involving the application of crushed silicate rocks, such as basalt, to agricultural soils with potential co-benefits for crops and soils, and mitigation of ocean acidification. Here we address the requirement of diverse stakeholders for informative studies quantifying possible environmental and health risks of Enhanced Rock Weathering. Using life-cycle assessment modelling of potential supply chain impacts for twelve nations undertaking Enhanced Rock Weathering deployment to deliver up to net 2 Gt CO2 yr−1 CDR, we find that rock grinding rather than mining exerts the dominant influence on environmental impacts. This finding holds under both a business-as-usual and clean energy mix scenario to 2050 but transitioning to undertaking Enhanced Rock Weathering in the future with low carbon energy systems improves the sustainability of the Enhanced Rock Weathering supply chain. We find that Enhanced Rock Weathering is competitive with other large-scale Carbon Dioxide Removal strategies in terms of energy and water demands.

We describe the development, tuning and climate of Planet Simulator (PLASIM)–Grid-ENabled Integrated Earth system model (GENIE), a new intermediate complexity Atmosphere–Ocean General Circulation Model (AOGCM), built by coupling the Planet Simulator to the ocean, sea-ice and land-surface components of the GENIE Earth system model. PLASIM–GENIE supersedes GENIE-2, a coupling of GENIE to the Reading Intermediate General Circulation Model (IGCM). The primitive-equation atmosphere includes chaotic, three-dimensional (3-D) motion and interactive radiation and clouds, and dominates the computational load compared to the relatively simpler frictional-geostrophic ocean, which neglects momentum advection. The model is most appropriate for long-timescale or large ensemble studies where numerical efficiency is prioritised, but lack of data necessitates an internally consistent, coupled calculation of both oceanic and atmospheric fields. A 1000-year simulation with PLASIM–GENIE requires approximately 2 weeks on a single node of a 2.1 GHz AMD 6172 CPU. We demonstrate the tractability of PLASIM–GENIE ensembles by deriving a subjective tuning of the model with a 50- member ensemble of 1000-year simulations. The simulated climate is presented considering (i) global fields of seasonal surface air temperature, precipitation, wind, solar and thermal radiation, with comparisons to reanalysis data; (ii) vegetation carbon, soil moisture and aridity index; and (iii) sea surface temperature, salinity and ocean circulation. Considering its resolution, PLASIM–GENIE reproduces the main features of the climate system well and demonstrates usefulness for a wide range of applications.

Asian Monsoon rainfall supports the livelihood of billions of people, yet the relative importance of different drivers remains an issue of great debate. Here, we present 30 million-year model-based reconstructions of Indian summer monsoon and South East Asian monsoon rainfall at millennial resolution. We show that precession is the dominant direct driver of orbital variability, although variability on obliquity timescales is driven through the ice sheets. Orographic development dominated the evolution of the South East Asian monsoon, but Indian summer monsoon evolution involved a complex mix of contributions from orography (39%), precession (25%), atmospheric CO2 (21%), ice-sheet state (5%) and ocean gateways (5%). Prior to 15 Ma, the Indian summer monsoon was broadly stable, albeit with substantial orbital variability. From 15 Ma to 5 Ma, strengthening was driven by a combination of orography and glaciation, while closure of the Panama gateway provided the prerequisite for the modern Indian summer monsoon state through a strengthened Atlantic meridional overturning circulation.

We describe the development of the “Paleoclimate PLASIM-GENIE (Planet Simulator–Grid-Enabled Integrated Earth system model) emulator” PALEO-PGEM and its application to derive a downscaled high-resolution spatio-temporal description of the climate of the last 5×106 years. The 5×106-year time frame is interesting for a range of paleo-environmental questions, not least because it encompasses the evolution of humans. However, the choice of time frame was primarily pragmatic; tectonic changes can be neglected to first order, so that it is reasonable to consider climate forcing restricted to the Earth's orbital configuration, ice-sheet state, and the concentration of atmosphere CO2. The approach uses the Gaussian process emulation of the singular value decomposition of ensembles of the intermediate-complexity atmosphere–ocean GCM (general circulation model) PLASIM-GENIE. Spatial fields of bioclimatic variables of surface air temperature (warmest and coolest seasons) and precipitation (wettest and driest seasons) are emulated at 1000-year intervals, driven by time series of scalar boundary-condition forcing (CO2, orbit, and ice volume) and assuming the climate is in quasi-equilibrium. Paleoclimate anomalies at climate model resolution are interpolated onto the observed modern climatology to produce a high-resolution spatio-temporal paleoclimate reconstruction of the Pliocene–Pleistocene.

Enhanced silicate rock weathering (ERW), deployable with croplands, has potential use for atmospheric carbon dioxide (CO2) removal (CDR), which is now necessary to mitigate anthropogenic climate change. ERW also has possible co-benefits for improved food and soil security, and reduced ocean acidification. Here we use an integrated performance modelling approach to make an initial techno-economic assessment for 2050, quantifying how CDR potential and costs vary among nations in relation to business-as-usual energy policies and policies consistent with limiting future warming to 2 degrees Celsius. China, India, the USA and Brazil have great potential to help achieve average global CDR goals of 0.5 to 2 gigatonnes of carbon dioxide (CO2) per year with extraction costs of approximately US$80–180 per tonne of CO2. These goals and costs are robust, regardless of future energy policies. Deployment within existing croplands offers opportunities to align agriculture and climate policy. However, success will depend upon overcoming political and social inertia to develop regulatory and incentive frameworks. We discuss the challenges and opportunities of ERW deployment, including the potential for excess industrial silicate materials (basalt mine overburden, concrete, and iron and steel slag) to obviate the need for new mining, as well as uncertainties in soil weathering rates and land–ocean transfer of weathered products.

At least six different Homo species populated the World during the latest Pliocene to the Pleistocene. The extinction of all but one of them is currently shrouded in mystery, and no consistent explanation has yet been advanced, despite the enormous importance of the matter. Here, we use a recently implemented past climate emulator and an extensive fossil database spanning 2,754 archaeological records to model climatic niche evolution in Homo. We find statistically robust evidence that the three Homo species representing terminating, independent lineages, H. erectus, H. heidelbergensis, and H. neanderthalensis, lost a significant portion of their climatic niche space just before extinction, with no corresponding reduction in physical range. This reduction coincides with increased vulnerability to climate change. In the case of Neanderthals, the increased extinction risk was probably exacerbated by competition with H. sapiens. This study suggests that climate change was the primary factor in the extinction of Homo species.

The transition to a low‐carbon economy will entail a large‐scale structural change. Some industries will have to expand their relative economic weight, while other industries, especially those directly linked to fossil fuel production and consumption, will have to decline. Such a systemic shift may have major repercussions on the stability of financial systems, via abrupt asset revaluations, defaults on debt, and the creation of bubbles in rising industries. Studies on previous industrial transitions have shed light on the financial transition risks originating from rapidly rising “sunrise” industries. In contrast, a similar conceptual understanding of risks from declining “sunset” industries is currently lacking. We substantiate this claim with a critical review of the conceptual and historical literature, which also shows that most literature either examines structural change in the real economy, or risks to financial stability, but rarely both together. We contribute to filling this research gap by developing a consistent theoretical framework of the drivers, transmission channels, and impacts of the phase‐out of carbon‐intensive industries on the financial system and on the feedback from the financial system into the rest of the economy. We also review the state of play of policy aiming to protect the financial system from transition risks and spell out research implications.

Individual processes shaping geographical patterns of biodiversity are increasingly understood, but their complex interactions on broad spatial and temporal scales remain beyond the reach of analytical models and traditional experiments. To meet this challenge, we built a spatially explicit, mechanistic simulation model implementing adaptation, range shifts, fragmentation, speciation, dispersal, competition, and extinction, driven by modeled climates of the past 800,000 years in South America. Experimental topographic smoothing confirmed the impact of climate heterogeneity on diversification. The simulations identified regions and episodes of speciation (cradles), persistence (museums), and extinction (graves). Although the simulations had no target pattern and were not parameterized with empirical data, emerging richness maps closely resembled contemporary maps for major taxa, confirming powerful roles for evolution and diversification driven by topography and climate.

A high degree of consensus exists in the climate sciences over the role that human interference with the atmosphere is playing in changing the climate. Following the Paris Agreement, a similar consensus exists in the policy community over the urgency of policy solutions to the climate problem. The context for climate policy is thus moving from agenda setting, which has now been mostly established, to impact assessment, in which we identify policy pathways to implement the Paris Agreement. Most integrated assessment models currently used to address the economic and technical feasibility of avoiding climate change are based on engineering perspectives with a normative systems optimisation philosophy, suitable for agenda setting, but unsuitable to assess the socio-economic impacts of realistic baskets of climate policies. Here, we introduce a fully descriptive, simulation-based integrated assessment model designed specifically to assess policies, formed by the combination of (1) a highly disaggregated macro-econometric simulation of the global economy based on time series regressions (E3ME), (2) a family of bottom-up evolutionary simulations of technology diffusion based on cross-sectional discrete choice models (FTT), and (3) a carbon cycle and atmosphere circulation model of intermediate complexity (GENIE). We use this combined model to create a detailed global and sectoral policy map and scenario that sets the economy on a pathway that achieves the goals of the Paris Agreement with >66% probability of not exceeding 2 °C of global warming. We propose a blueprint for a new role for integrated assessment models in this upcoming policy assessment context.