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Data on income distributions within and across countries are becoming increasingly important to inform analysis of income inequality and to understand the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same income concept and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the GINI coefficient. For the imputation, we use a recently developed principal components-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions and the world as a whole. Our aggregation method takes into account both within-country and across-country income inequality when aggregating to the regional level. This dataset will enable more robust analysis of income distribution at multiple scales.

Background: Spatial population models are important to inform understanding of historical demographic development patterns and to project possible future changes, especially for use in anticipating environmental interactions. Objective: We document, calibrate, and evaluate a high-resolution gravity-based population downscaling model for each US state and interpret its historical urban and rural spatial population change patterns. Methods: We estimate two free parameters that govern the spatial population change pattern using the historical population grids of each state. We interpret the resulting parameters in light of the spatial development pattern they represent. We evaluate the model by comparing the resulting total population grid of each state in 2010 against its census-based grid. We also analyze the temporal stability of parameters across the 1990–2000 and 2000–2010 decades. Results: Our analysis indicates varying levels of performance across states and population types. While our results suggest a consolidated change pattern in urban population across states, rural population change patterns are diverse. We find urban parameters are more stable. Conclusions: The model’s adaptability, performance, and interpretability indicate its potential for depicting historical state-level spatial population changes. It assigns these changes to different representative categories to assist interpretation. Contribution: We document and evaluate a gravitational model as well as investigate historical state-level spatial population changes. This research facilitates future work creating projections of the spatial distribution of population at the subnational level, especially those according to the Shared Socioeconomic Pathways (SSPs), widely used scenarios for climate change research.

We parallelize the extended path method for solving rational expectations models, and apply it to compute perfect foresight competitive equilibrium for the global economy with multiple goods, regions, industries, and households. At each iteration, first intertemporal variables are updated, then equations for intra-temporal variables are solved in parallel. We compare serial, and parallel, versions of the extended path method in high-performance computing environments based on scenarios with long time horizons that include future populations, economic growth, energy use, and carbon dioxide emissions. Relative to the serial version, the speedup factor for the parallel extended path method grows almost linearly up to about 30 times with 18 cores, and computing times reduced from over 10 h for the serial version down to about 20 min for the parallel version.

The Intergovernmental Panel on Climate Change (IPCC) reports provide policy-relevant insights about climate impacts, vulnerabilities and adaptation through a process of peer-reviewed literature assessments underpinned by expert judgement. An iconic output from these assessments is the burning embers diagram, first used in the Third Assessment Report to visualize reasons for concern, which aggregate climate-change-related impacts and risks to various systems and sectors. These burning embers use colour transitions to show changes in the assessed level of risk to humans and ecosystems as a function of global mean temperature. In this Review, we outline the history and evolution of the burning embers and associated reasons for concern framework, focusing on the methodological approaches and advances. While the assessment framework and figure design have been broadly retained over time, refinements in methodology have occurred, including the consideration of different risks, use of confidence statements, more formalized protocols and standardized metrics. Comparison across reports reveals that the risk level at a given temperature has generally increased with each assessment cycle, reflecting accumulating scientific evidence. For future assessments, an explicit, transparent and systematic process of expert elicitation is needed to enhance comparability, quality and credibility of burning embers.

We consider alternative history scenarios in which explicit climate mitigation begins before the present day, estimating the total costs to date of delayed action. Considering a 2(1.5) degree Celsius stabilization target, peak costs are greater and reached sooner with a later start to mitigation, reaching 15(17)% of global GDP in 2085(2070) for a 1990 start and 18(35)% in 2080(2035) for a 2020 start. Further mitigation delay costs a best estimate of an additional 0.5(5) trillion dollars per year. Additional simulations show how optimal mitigation pathways evolve without imposing a warming limit, finding that median abatement levels and costs are not strongly dependent on start date. However, whereas 18(5) percent of optimal solutions starting in 1980 meet the 2(or 1.5) degree target, 5(or 0)% of 2020 simulations meet the goals. Discounted damages due to delayed mitigation action rise by 0.6 trillion US dollars per year in 2020.

Increased educational attainment is a sustainable development priority and has been posited to have benefits for other social and environmental issues, including climate change. However, links between education and climate change risks can involve both synergies and trade-offs, and the balance of these effects remains ambiguous. Increases in educational attainment could lead to faster economic growth and therefore higher emissions, more climate change and higher risks. At the same time, improved attainment would be associated with faster fertility decline in many countries, slower population growth and therefore lower emissions, and would also be likely to reduce vulnerability to climate impacts. We employ a multiregion, multisector model of the world economy, driven with country-specific projections of future population by level of education, to test the net effect of education on emissions and on the Human Development Index (HDI), an indicator that correlates with adaptive capacity to climate impacts. We find that improved educational attainment is associated with a modest net increase in emissions but substantial improvement in the HDI values in developing country regions. Avoiding stalled progress in educational attainment and achieving gains at least consistent with historical trends is especially important in reducing future vulnerability. The effect of education on climate change risks is integral to the relation between climate and development, but difficult to quantify. This article finds potential increases in emissions as well as HDI values due to improved educational attainment in developing countries.

Climate change could threaten sub-Saharan Africa's progress on human development, but impact studies often focus on intermediate effects instead. We conduct a systematic literature review of projected climate impacts on three important dimensions of human development: food insecurity, water stress, and disease. Despite a large impact literature, only 28 studies project quantitative outcomes for these dimensions of development, and even these studies sometimes focus on proxies for outcomes of interest. Studies forecast that climate change could reduce hunger by

Understanding and projecting income distributions within countries and regions is important to understanding consumption trends and the distributional consequences of climate impacts and responses. Several global, country-level projections of income distribution are available but most project only the Gini coefficient (a summary statistic of the distribution) or utilize the Gini along with the assumption of a lognormal distribution. We test the lognormal assumption and find that it typically underestimates income in the highest deciles and over-estimates it in others. We find that a new model based on two principal components of national time series data for income distribution provides a better fit to the data for all deciles, especially for the highest and lowest. We also construct a projection model in which the first principal component is driven by the Gini coefficient and the second captures deviations from this relationship. We use the model to project income distribution by decile for all countries for the five shared socioeconomic pathways. We find that inequality is consistently higher than projections based on the Gini and the lognormal functional form, with some countries reaching ratios of the highest to lowest income deciles that are almost three times their value using the lognormal assumption.

Population concentration and built‐up land expansion are two prominent features of contemporary urbanization. Existing literature on the population aspect of urbanization has mostly focused on national and regional aggregates, and literature on the land development aspect has often relied on spatial case studies of individual cities or their meta‐analyses. Using newly‐available data, here we conduct the first global‐coverage, spatial analysis of the relationship between (changes in) population and built‐up land at multiple spatial scales, and compare to existing common beliefs about urbanization based on individual city studies. We find that population and built‐up land show distinctly different spatial and temporal patterns (with a global correlation coefficient around 0.6). Contrary to common impressions, our results show that during recent decades, developed and developing regions across the world experienced comparable amounts of built‐up land expansion. While meta‐analyses have reported that built‐up land in urban areas expands globally on average twice as fast as population grows, our results show the global change rates of built‐up land and population are similar. Also, most global population, including what national statistics agencies call urban population, reside in areas with low land development levels (which are frequently less than 5% built up). These changes in perspective suggest that urbanization's potential large‐scale impacts may need to be re‐evaluated, and lead to best‐practice recommendations for urbanization modeling and analysis. Especially, the common practice in large‐scale earth system modeling of assuming demographically‐defined urban population resides in areas with medium to high built‐up land development levels should change.

While every society can be exposed to heatwaves, some people suffer far less harm and recover more quickly than others from their occurrence. Here we project indicators of global heatwave risk associated with global warming of 1.5 and 2 °C, specified by the Paris agreement, for two future pathways of societal development representing low and high vulnerability conditions. Results suggest that at the 1.5 °C warming level, heatwave exposure in 2075 estimated for the population living in low development countries is expected to be greater than exposure at the warming level of 2 °C for the population living in very high development countries. A similar result holds for an illustrative heatwave risk index. However, the projected difference in heatwave exposure and the illustrative risk index for the low and very high development countries will be significantly reduced if global warming is stabilized below 1.5 °C, and in the presence of rapid social development., Climate extremes, exposure and vulnerability all contribute to global difference in heatwave risk. Here the authors investigated the inequality in global heatwave risk under both 1.5 and 2 °C scenarios and found that heatwave risk for the poor under 1.5 °C scenario exceeds that risk for the rich under 2 °C scenario.

Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categories of increasingly complex climate change risk that focus on interactions among the multiple drivers of risk, as well as among multiple risks. A significant innovation is recognizing that risks can arise both from potential impacts due to climate change and from responses to climate change. This approach encourages thinking that traverses sectoral and regional boundaries and links physical and socio-economic drivers of risk. Advancing climate change risk assessment in these ways is essential for more informed decision making that reduces negative climate change impacts. © 2021 The Authors

To better understand the role projected land‐use changes (LUCs) may play in future regional climate projections, we assess the combined effects of greenhouse‐gas (GHG)‐forced climate change and LUCs in regional climate model (RCM) simulations. To do so, we produced RCM simulations that are complementary to the North‐American Coordinated Regional Downscaling Experiment (NA‐CORDEX) simulations, but with future LUCs that are consistent with particular Shared Socioeconomic Pathways (SSPs) and related to a specific Representative Concentration Pathway (RCP). We examine the state of the climate at the end of the 21st century with and without two urban and agricultural LUC scenarios that follow SSP3 and SSP5 using the Weather Research and Forecasting (WRF) model forced by one global climate model, the MPI‐ESM, under the RCP8.5 scenario. We find that LUCs following different societal trends under the SSPs can significantly affect climate projections in different ways. In regions of significant cropland expansion over previously forested area, projected annual mean temperature increases are diminished by around 0.5°C–1.0°C. Across all seasons, where urbanization is high, projected temperature increases are magnified. In particular, summer mean temperature projections are up to 4°C–5°C greater and minimum and maximum temperature projections are increased by 2.5°C–6°C, amounts that are on par with the warming due to GHG‐forced climate change. Warming is also enhanced in the urban surroundings. Future urbanization also has a large influence on precipitation projections during summer, increasing storm intensity, event length, and the overall amount over urbanized areas, and decreasing precipitation in surrounding areas.

In order to assess the combined effects of green-house-gas-induced climate change and land-use land-cover change (LULCC), we have produced regional climate model (RCM) simulations that are complementary to the North-American Coordinated Regional Downscaling Experiment (NA-CORDEX) simulations, but with future LULCCs that are consistent with particular Shared Socioeconomic Pathways (SSPs). In standard, existing NA-CORDEX simulations, land surface characteristics are held constant at present day conditions. These new simulations, in conjunction with the NA-CORDEX simulations, will help us assess the magnitude of the changes in regional climate forced by LULCC relative to those produced by increasing greenhouse gas concentrations. Understanding the magnitude of the regional climate effects of LULCC is important to the SSP-RCP scenarios framework. Whether or not the pattern of climate change resulting from a given SSP-RCP pairing is sensitive to the pattern of LULCC is an understudied problem. This work helps address this question, and will inform thinking about possible needed modifications to the scenarios framework to better account for climate-land use interactions.Accordingly, in this presentation, we will examine the state of the climate at the end of the 21st century with and without SSP-driven LULCCs in RCM simulations produced using WRF under the RCP8.5 concentration scenario. The included LULCC change effects have been created following the SSP3 and SSP5 narratives using an existing agricultural land model linked with a new long-term spatial urban land model.

Following the conceptual risk framing of the IPCC that defines risk as a function of hazard, exposure and vulnerability, we estimate global heatwave risk by using a statistical approach that combines the distribution of indicators for heatwave magnitude, population exposure and human development. We fit a general extreme value distribution (GEV) to the maxima of the heatwave magnitude index in a block of 10-years to estimate extreme heatwave conditions with a 500yr return period under current climate (HW500yr). To consider the impact of changes in heatwaves in populated regions of the world, we use a set of global, spatially explicit population projections that are consistent with the new Shared Socioeconomic Pathways (SSPs). As a proxy for vulnerability we use the Human Development Index (HDI) based on the geometric average of three dimensions: health, education and standard of living. We derive an illustrative heatwave risk indicator (expressed in %) for each location (i.e. grid box) of the globe as the product of the probability of occurrence of HW500Y multiplied by normalized population density and 1-HDI values with all components of the product being normalized. Using this illustrative heatwave risk indicator on a global scale, we project heatwave risk for global warming of 1.5 and 2 °C, in accordance with the Paris agreement, for two future pathways of societal development representing low and high vulnerability conditions (SSP1 and SSP4, respectively). This method demonstrates how including a measure of vulnerability could produce a distribution of risk that is different from the distribution of hazard or exposure under different scenarios. Our results show that the heatwave risk for the low and very high development countries would be significantly reduced if global warming is stabilized below 1.5 °C, and in the presence of rapid social development. The latter is most important for low development countries for decreasing their vulnerability towards heatwaves or other hazards being amplified by climate change. The results illustrate how hazard-specific policies could be better informed by analyses that account for vulnerabilities to the respective hazard. However, we also discuss several caveats associated with using a normalized risk indicator on a global scale with implications for the interpretation of risk on a local scale, including the need for better indicators to describe vulnerability to specific or multiple hazards.

In this chapter, we introduce the integrated scenario framework that currently underpins future climate modeling, impact assessment, and mitigation studies. We describe the basic conceptual design of the shared socioeconomic pathways (SSP) and their role in the generation of future emission pathways. This will elucidate the origin and rationale of the pathways used as external forcings for climate model simulations, both the past representative concentration pathways and the design of the current set used by CMIP6 experiments. From a complementary perspective, we describe the development and availability of SSP elements that in addition to driving the production of external forcings can (should) be used to characterize and quantify the human dimension of future change. Projections of population and gross domestic product growth, demographic characteristics and income distribution, assumptions about adaptation capacity, technology development, etc. are available at a range of spatial and temporal scales and should enable a holistic assessment of regional and sectoral impacts taking into account both the physical and socioeconomic drivers of change. A brief review of the existing recent literature utilizing the integrated approach and spanning a wide range of impact domains is given, and over this background, specific examples of analysis integrating the physical and human dimension of change using the available scenarios are provided for a number of different sectors: public health (e.g., population exposure to heat extremes); agricultural production (e.g., changes in food prices and trade driven by productivity changes due to climate); coastal areas exposure to hazards (e.g., changes in structures at risk from tropical cyclone changes in intensity and frequency).

The Paris Agreement stipulates that global warming be stabilized at well below 2 °C above pre-industrial levels, with aims to further constrain this warming to 1.5 °C. However, it also calls for reducing net anthropogenic greenhouse gas (GHG) emissions to zero during the second half of this century. Here, we use a reduced-form integrated assessment model to examine the consistency between temperature- and emission-based targets. We find that net zero GHG emissions are not necessarily required to remain below 1.5 °C or 2 °C, assuming either target can be achieved without overshoot. With overshoot, however, the emissions goal is consistent with the temperature targets, and substantial negative emissions are associated with reducing warming after it peaks. Temperature targets are put at risk by late achievement of emissions goals and the use of some GHG emission metrics. Refinement of Paris Agreement emissions goals should include a focus on net zero CO2—not GHG—emissions, achieved early in the second half of the century. While well-known for its temperature targets, the Paris Agreement also aims for net zero GHG emissions. IAM results reveal net zero GHG emissions are not always required to meet the temperature targets, and that net zero CO2 emissions are a more suitable aim.

Heat waves are among the most dangerous climate-related hazards, and they are projected to increase in frequency and intensity over the coming century. Exposure to heat waves is a function of the spatial distribution of physical events and the corresponding population distribution, and future exposure will be impacted by changes in both distributions. Here, we project future exposure using ensembles of climate projections that account for the urban heat island effect, for two alternative emission scenarios (RCP4.5/RCP8.5) and two alternative population and urbanization (SSP3/SSP5) outcomes. We characterize exposure at the global, regional, and grid-cell level; estimate the exposure that would be avoided by mitigating future levels of climate change (to RCP4.5); and quantify the dependence of exposure on population outcomes. We find that climate change is a stronger determinant of exposure than demographic change in these scenarios, with a global reduction in exposure of over 50% under a lower emissions pathway, while a slower population growth pathway leads to roughly 30% less exposure. Exposure reduction varies at the regional level, but in almost all cases, the RCP remains more influential than the SSP. Uncertainty in outcomes is dominated by inter-annual variability in heat extremes (relative to variability across initial condition ensemble members). For some regions, this variability is large enough that a reduction in annual exposure is not guaranteed in each individual year by following the lower forcing pathway. Finally, we find that explicitly considering the urban heat island effect and separate urban and rural heat extremes and populations can substantially influence results, generally increasing projected exposure.

We present a parallel parameter optimization algorithm for reproducing future projections of certain model outputs in dynamic general equilibrium models. The optimization problem is reduced to a nonlinear system of equations. The Jacobian matrix for a Newton-type solver in the problem is generated in parallel. The parameter optimization algorithm is implemented for parallel systems with distributed memory by using MPI. To achieve better performance of the parallel algorithm we use the parallel Fair – Taylor algorithm for computing an equilibrium path. Calculation of prices, input-output ratios and international trade for different time steps is carried out in parallel at each iteration of the method. The solution method is implemented for parallel systems with shared memory by using OpenMP. The effectiveness of the hybrid MPI+OpenMP parallel code for parameter optimization is demonstrated in the example of a global multi-sector energy economics model with scenarios that are used for studying climate change impacts on land use.

Understanding how impacts may differ across alternative levels of future climate change is necessary to inform mitigation and adaptation measures. The Benefits of Reduced Anthropogenic Climate changE (BRACE) project assesses the differences in impacts between two specific climate futures: a higher emissions future with global average temperature increasing about 3.7 °C above pre-industrial levels toward the end of the century and a moderate emissions future with global average warming of about 2.5 °C. BRACE studies in this special issue quantify avoided impacts on physical, managed, and societal systems in terms of extreme events, health, agriculture, and tropical cyclones. Here we describe the conceptual framework and design of BRACE and synthesize its results. Methodologically, the project combines climate modeling, statistical analysis, and impact assessment and draws heavily on large ensembles using the Community Earth System Model. It addresses uncertainty in future societal change by employing two pathways for future socioeconomic development. Results show that the benefits of reduced climate change within this framework vary substantially across types of impacts. In many cases, especially related to extreme heat events, there are substantial benefits to mitigation. The benefits for some heat extremes are statistically significant in some regions as early as the 2020s and are widespread by mid-century. Benefits are more modest for agriculture and exposure to some health risks. Benefits are negative for agriculture when CO2 fertilization is incorporated. For several societal impacts, the effect on outcomes of alternative future societal development pathways is substantially larger than the effect of the two climate scenarios.

Downscaling methods for dynamic computable general equilibrium models are developed and analyzed. The methods produce outcomes for a variety of different household types by downscaling the aggregate quantities from an economic growth model with a representative household. This approach uses household survey data and long-term population projections for different household types to compare the performance of the downscaling methods vs. a general equilibrium model with multiple household groups under a variety of conditions, including demographic change, technological change, and a carbon tax. Both recursive-dynamic and forward-looking downscaling methods produce results that approximate well a multiple household model run. The recursive-dynamic downscaling method is applied to an illustrative example estimating impacts of a carbon tax on aggregate CO 2 emissions and the energy demand of different household groups for a middle of the road development scenario.

The new scenario process for climate change research includes the creation of Shared Socioeconomic Pathways (SSPs) describing alternative societal development trends over the coming decades. Urbanization is a key aspect of development that is relevant to studies of mitigation, adaptation, and impacts. Incorporating urbanization into the SSPs requires a consistent set of global urbanization projections that cover long time horizons and span a full range of uncertainty. Existing urbanization projections do not meet these needs, in particular providing only a single scenario over the next few decades, a period during which urbanization is likely to be highly dynamic in many countries. We present here a new, long-term, global set of urbanization projections at country level that cover a plausible range of uncertainty. We create SSP-specific projections by choosing urbanization outcomes consistent with each SSP narrative. Results show that the world continues to urbanize in each of the SSPs but outcomes differ widely across them, with urbanization reaching 60%, 79%, and 92% by the end of century in SSP3, SSP2, and SSP1/SSP4/SSP5, respectively. The degree of convergence in urbanization across countries also differs substantially, with largely convergent outcomes by the end of the century in SSP1 and SSP5 and persistent diversity in SSP3. This set of global, country-specific projections produces urbanization pathways that are typical of regions in different stages of urbanization and development levels, and can be extended to further elaborate assumptions about the styles of urban growth and spatial distributions of urban people and land cover occurring in each SSP.

Population policies offer options to lessen climate risks

Spatially-explicit population projections by age are increasingly needed for understanding bilateral human–environment interactions. Conventional demographic methods for projecting age structure experience substantial challenges at small spatial scales. In search of a potentially better-performing alternative, we develop an empirically based spatial model of population age structure and test its application in projecting US population age structure over the 21st century under various socioeconomic scenarios (SSPs). The model draws on 40 years of historical data explaining changes in spatial age distribution at the county level. It demonstrates that a very good model fit is achievable even with parsimonious data input, and distinguishes itself from existing methods as a promising approach to spatial age structure modeling at the global level where data availability is often limited. Results suggest that wide variations in the spatial pattern of county-level age structure are plausible, with the possibility of substantial aging clustered in particular parts of the country. Aging is experienced most prominently in thinly populated counties in the Midwest and the Rocky Mountains, while cities and surrounding counties, particularly in California, as well as the southern parts of New England and the Mid-Atlantic region, maintain a younger population age structure with a lower proportion in the most vulnerable 70+ age group. The urban concentration of younger people, as well as the absolute number of vulnerable elderly people can vary strongly by SSP.

Earth system models (ESMs) are the primary tool used to understand and project changes to the climate system. ESM projections underpin analyses of human dimensions of the climate issue, yet little is known about how ESMs are used in human dimensions research. Such foundational information is necessary for future critical assessments of ESMs. We review applications of a leading ESM, the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM), to human dimensions topics since 2004. We find that this research has grown substantially over this period, twice as fast as CESM research overall. Although many studies have primarily addressed long‐term impacts on physical systems with societal relevance, applications to managed, societal, and ecological systems have grown quickly and now make up more than half of CESM human dimensions work. CESM applications focused nearly equally on global and regional analyses, most often using multimodel ensembles, although the use of single simulations remains prevalent. Downscaling and bias correction of output was infrequent and most common for regional studies. U.S.‐based, university‐affiliated authors primarily drove human dimensions work using CESM, with only 12% of authors based at NCAR. Our findings identify important questions that warrant further investigation, such as reasons for the infrequent use of downscaling and bias correction techniques; motivations to continue to use older model versions after newer model versions have been released; and model development needs for improved human dimensions applications. Additionally, our synthesis provides a baseline and framework that enables continued tracking of CESM and other ESMs. This article is categorized under: Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

In the version of this Perspective originally published, the footnote of Table 1 incorrectly described the meanings of symbols in the table, and the tick marks paired with obeli symbols should have been + signs. The symbols have been amended, as has the footnote, which now reads “Tick marks indicate recommendations made to address the issue in the indicated column; + signs indicate benefits to additional issues”. The online versions of the Perspective have been corrected.

Crop yields are vulnerable to climate change. We assess the global impacts of climate change on agricultural systems under two climate projections (RCP8.5 and RCP4.5) to quantify the difference in impacts if climate change were reduced. We also employ two different socioeconomic pathways (SSP3 and SSP5) to assess the sensitivity of results to the underlying socioeconomic conditions. The integrated-Population-Economy-Technology-Science (iPETS) model, a global integrated assessment model for projecting future energy use, land use and emissions, is used in conjunction with the Community Earth System Model (CESM), and particularly its land surface component, the Community Land Model (CLM), to evaluate climate change impacts on agriculture. iPETS results are produced at the level of nine world regions for the period 2005–2100. We employ climate impacts on crop yield derived from CLM, driven by CESM simulations of the two RCPs. These yield effects are applied within iPETS, imposed on baseline and mitigation scenarios for SSP3 and SSP5 that are consistent with the RCPs. We find that the reduced level of warming in RCP4.5 (relative to RCP8.5) can have either positive or negative effects on the economy since crop yield either increases or decreases with climate change depending on assumptions about CO2 fertilization. Yields are up to 12 % lower, and crop prices are up to 15 % higher, in RCP4.5 relative to RCP8.5 if CO2 fertilization is included, whereas yields are up to 22 % higher, and crop prices up to 22 % lower, if it is not. We also find that in the mitigation scenarios (RCP4.5), crop prices are substantially affected by mitigation actions as well as by climate impacts. For the scenarios we evaluated, the development pathway (SSP3 vs SSP5) has a larger impact on outcomes than climate (RCP4.5 vs RCP8.5), by a factor of 3 for crop prices, 11 for total cropland use, and 35 for GDP on global average.

Decarbonization in the immediate future is required to limit global mean temperature (GMT) increase to 2 degrees C relative to pre-industrial conditions, if geoengineering is not considered. Here we use the Community Earth System Model (CESM) to investigate climate outcomes if no mitigation is undertaken until GMT has reached 2 degree C. We find that late decarbonization (LD) in CESM without applying stratospheric sulfur injection (SSI) leads to a peak temperature increase of 3 degree C and GMT remains above 2 degrees for 160 years. An additional gradual increase and then decrease of SSI over this period reaching about 1.5 times the aerosol burden resulting from the Mt Pinatubo eruption in 1992 would limit the increase in GMT to 2.0 degrees for the specific pathway and model. SSI produces mean and extreme temperatures in CESM comparable to an early decarbonization pathway, but aridity is not mitigated to the same extent.

The 2015 Paris Agreement aims to limit warming to 2 or 1.5°C above preindustrial level, although combined Intended Nationally Determined Contributions (INDCs) are likely insufficient to achieve these targets. We propose a set of idealized emission pathways consistent with the targets. If countries reduce emissions in line with their INDCs, the 2°C threshold could be avoided only if net zero greenhouse gas emissions (GHGEs) are achieved by 2085 and late century negative emissions are considerably in excess of those assumed in Representative Concentration Pathway (RCP) 2.6 (net −5 Gt CO2/yr, compared with −1.5 Gt CO2/yr in RCP2.6). More aggressive near-term reductions would allow 2°C to be avoided with less end-of-century carbon removal capacity. A 10% cut in GHGEs by 2030 (relative to 2015) could likely achieve 2°C with RCP2.6 level negative emissions. The 1.5°C target requires GHGEs to be reduced by almost a third by 2030 and net zero by 2050, while a 50 year overshoot of 1.5°C allows net zero GHGEs by 2060.

There is a growing demand for subnational population projections for informing potential demographic influences on many aspects of society and the environment at the scale at which interactions occur and actions are taken. Existing US subnational population projections have not fully accounted for regional variations of demographic rates and therefore under-estimate the uncertainties in and heterogeneity of population trends. We present a first set of population projections for US states that span a wide but plausible range of population outcomes driven by changing state-level demographic rates consistent with the widely used SSP scenario framework. The projections are carried out for all 50 states integrated through bilateral gross migration flows. They update the original national-level SSP population projections based on recently available data and introduce more plausible assumptions on long-term international migration. We project a national population ranging from about 250–650 million by 2100, somewhat lower than the SSP projections due mainly to updated base year data. Utah and other states in the Rocky Mountain region see the largest increases in population in proportional terms, while the Northeast and Great Lakes regions see the slowest growth or most decline, along with individual states like Alaska, California, Louisiana, and Mississippi. Aging occurs in all states and scenarios, but is most prominent in the Northeast, Florida, and in some cases states in the West and the Great Lakes region. The relative contributions of fertility, mortality, and migration to population change varies substantially across states.

Spatial population distribution is an important determinant of both drivers of regional environmental change and exposure and vulnerability to it. Spatial projections of population must account for changes in aggregate population, urbanization, and spatial patterns of development, while accounting for uncertainty in each. While an increasing number of projections exist, those carried out at relatively high resolution that account for subnational heterogeneity and can be tailored to represent alternative scenarios of future development are rare. We draw on state-level population projections for the US and a gravity-style spatial downscaling model to design and produce new spatial projections for the U.S. at 1 km resolution consistent with a subset of the Shared Socioeconomic Pathways (SSPs), scenarios of societal change widely used in integrated analyses of global and regional change. We find that the projections successfully capture intended alternative development patterns described in the SSPs, from sprawl to concentrated development and mixed outcomes. Our projected spatial patterns differ more strongly across scenarios than in existing projections, capturing a wider range of the relevant uncertainty introduced by the distinct scenarios. These projections provide an improved basis for integrated environmental analysis that considers uncertainty in demographic outcomes.

There is growing evidence that the role internal variability plays in our confidence in future climate projections has been under-appreciated in past assessments of model projections for the coming decades. In light of this, a 15 member ensemble has been produced to complement the existing 30 member “Large Ensemble” conducted with the Community Earth System Model (CESM). In contrast to the Large Ensemble, which explored the variability in RCP8.5, our new ensemble uses the moderate mitigation scenario represented by RCP4.5. By comparing outputs from these two ensembles, we assess at what point in the future the climates conditioned on the two scenarios will begin to significantly diverge. We find in general that while internal variability is a significant component of uncertainty for periods before 2050, there is evidence of a significantly increased risk of extreme warm events in some regions as early as 2030 in RCP8.5 relative to RCP4.5. Furthermore, the period 2061-2080 sees largely separate joint distributions of annual mean temperature and precipitation in most regions for the two ensembles. Hence, in the CESM’s representation of the Earth System for the latter portion of the 21st century, the range of climatic states which might be expected in the RCP8.5 scenario is significantly and detectably further removed from today’s climate state than the RCP4.5 scenario even in the presence of internal variability.

The consequences of climate policy and the impacts of climate change vary among different types of households depending on their income level, expenditure pattern, and other socioeconomic characteristics. Global economy-environment models that are used to assess climate change issues traditionally do not distinguish households by income or other attributes. To facilitate progress in this area, we review and assess literature on methods to include household heterogeneity in global long-term Computable General Equilibrium (CGE) models. We distinguish among three categories of approaches: 1) the explicit modeling of multiple household types within the CGE framework, 2) micro-simulation modeling, and 3) direct modeling of income distribution. For each of these approaches we describe the method, key assumptions, limitations and several prominent examples from the literature. Moreover, we discuss data needs, including the contents of household survey data, their availability and processing. We conclude with an overview of what each method could provide for global, long-term climate-related research.

US population exposure to extreme heat is set to increase four- to sixfold from the late twentieth century. Changes in population are as important as changes in climate in driving this outcome.

A key uncertainty in projecting future climate change is the magnitude of equilibrium climate sensitivity (ECS), that is, the eventual increase in global annual average surface temperature in response to a doubling of atmospheric CO2 concentration. The lower bound of the likely range for ECS given in the IPCC Fifth Assessment Report (AR5; refs 1,2) was revised downwards to 1.5 degrees C, from 2 degrees C in its previous report(3), mainly as an effect of considering observations over the warming hiatus-the period of slowdown of global average temperature increase since the early 2000s. Here we analyse how estimates of ECS change as observations accumulate over time and estimate the contribution of potential causes to the hiatus. We find that including observations over the hiatus reduces the most likely value for ECS from 2.8 degrees C to 2.5 degrees C, but that the lower bound of the 90% range remains stable around 2 degrees C. We also find that the hiatus is primarily attributable to El Nino/Southern Oscillation-related variability and reduced solar forcing.

The reasons for concern framework communicates scientific understanding about risks in relation to varying levels of climate change. The framework, now a cornerstone of the IPCC assessments, aggregates global risks into five categories as a function of global mean temperature change. We review the framework's conceptual basis and the risk judgments made in the most recent IPCC report, confirming those judgments in most cases in the light of more recent literature and identifying their limitations. We point to extensions of the framework that offer complementary climate change metrics to global mean temperature change and better account for possible changes in social and ecological system vulnerability. Further research should systematically evaluate risks under alternative scenarios of future climatic and societal conditions.