Table of contents

The aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socio-economic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.

Life-cycle assessment (LCA) is an established methodology that can provide decision-makers with comprehensive data on the environmental impacts of products and processes during the entire life cycle. However, the literature on building LCAs consists of highly varying results between the studies, even when the assessed buildings are very similar. This makes it doubtful if LCA can actually produce reliable data for supporting policy-making in the building sector. However, no prior reviews looking into this issue in the building sector exist. This study includes an extensive literature review of LCA studies on the pre-use phase of buildings. The purpose of this study is to analyze the variation between the results of different studies and find out whether the differences can be explained by the contextual differences or if it is actually the methodological choices that cause the extremely high variation. We present 116 cases from 47 scientific articles and reports that used process LCA, input–output (IO) LCA or hybrid LCA to study the construction-phase GHG emissions of buildings. The results of the reviewed studies vary between 0.03 and 2.00 tons of GHG emissions per gross area. The lowest was assessed with process LCA and highest with IO LCA, and in general the lower end was found to be dominated by process LCA studies and the higher end by IO LCA studies, hybrid LCAs being placed in between. In general, it is the methodological issues and subjective choices of the LCA practitioner that cause the vast majority of the huge variance in the results. It thus seems that currently the published building LCAs do not offer solid background information for policy-making without deep understanding of the premises of a certain study and good methodological knowledge.

Understanding climate risk is crucial for effective adaptation action, and a number of assessment methodologies have emerged. We argue that the dynamics of the individual components in climate risk and vulnerability assessments has received little attention. In order to highlight this, we systematically reviewed 42 sub-national climate risk and vulnerability assessments. We analysed the assessments using an analytical framework with which we evaluated (1) the conceptual approaches to vulnerability and exposure used, (2) if current or future risks were assessed, and (3) if and how changes over time (i.e. dynamics) were considered. Of the reviewed assessments, over half addressed future risks or vulnerability; and of these future-oriented studies, less than 1/3 considered both vulnerability and exposure dynamics. While the number of studies that include dynamics is growing, and while all studies included socio-economic aspects, often only biophysical dynamics was taken into account. We discuss the challenges of assessing socio-economic and spatial dynamics, particularly the poor availability of data and methods. We suggest that future-oriented studies assessing risk dynamics would benefit from larger stakeholder involvement, discussion of the assessment purpose, the use of multiple methods, inclusion of uncertainty/sensitivity analyses and pathway approaches.

Distinct biases are found in the pattern and teleconnections of the Arctic Oscillation (AO) in 32 climate models that participate the Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared with observations, the Pacific (Atlantic) center of AO is excessively strong (weak) in most of the 32 CMIP5 models, and the AO-related surface air temperature anomalies are generally weak over the Eurasian continent and North America. These biases are closely tied to the excessively strong linkage, which is marginal in observations, between AO and the North Pacific mode (NPM)—the leading variability of the North Pacific sea level pressure. It implies that the AO in CMIP5 models may be compounded with some regional mode over the North Pacific. Accordingly, a bias-correction method was proposed via correcting the AO index (AOI) to improve the diagnostic estimates of the AO teleconnections. The results suggest that the biases in the pattern and teleconnections of AO can be significantly reduced when the NPM variability is linearly removed from the AOI.

The environmental argument behind fossil fuel subsidy reform is strong, particularly among international finance institutions wishing to support 'transformational' low-carbon development. However, supporting reform in practice has often met methodological and political barriers. Instead, a large share of international climate finance has flowed to national policies and measures that incentivize the deployment of low-carbon technologies such as renewable energy technologies. In this paper, we propose that 'hybrid' policies that package fossil fuel subsidy reform with low-carbon technology deployment policy offer an opportunity for donors to support mitigation activities that achieve both concrete environmental impacts as well as long-term structural change. Specifically, we model the abatement cost, fossil fuel subsidy savings, and generation cost resulting from combining wind and solar photovoltaic deployment policy with fossil fuel subsidy phase-out in four country case studies. Our results not only show the extent to which fossil fuel subsidies can undermine the financial viability of low-carbon energy technologies, but also how cost uncertainties can be buffered by combining fossil fuel subsidy reform with renewable energy deployment. Furthermore, we assess the proposed hybrid policy against typical climate finance criteria and thus contribute to debates surrounding donor strategies to support low-carbon development.

We provide evidence that lower fertility can simultaneously increase income per capita and lower carbon emissions, eliminating a trade-off central to most policies aimed at slowing global climate change. We estimate the effect of lower fertility on carbon emissions, accounting for the fact that changes in fertility patterns affect carbon emissions through three channels: total population, the age structure of the population, and economic output. Our analysis proceeds in two steps. First, we estimate the elasticity of carbon emissions with respect to population and income per capita in an unbalanced yearly panel of cross-country data from 1950–2010. We demonstrate that the elasticity with respect to population is nearly seven times larger than the elasticity with respect to income per capita and that this difference is statistically significant. Thus, the regression results imply that 1% slower population growth could be accompanied by an increase in income per capita of nearly 7% while still lowering carbon emissions. In the second part of our analysis, we use a recently constructed economic-demographic model of Nigeria to estimate the effect of lower fertility on carbon emissions, accounting for the impacts of fertility on population growth, population age structure, and income per capita. We find that by 2100 C.E. moving from the medium to the low variant of the UN fertility projection leads to 35% lower yearly emissions and 15% higher income per capita. These results suggest that population policies could be part of the approach to combating global climate change.

Global vegetation models and terrestrial carbon cycle models are widely used for projecting the carbon balance of terrestrial ecosystems. Ensembles of such models show a large spread in carbon balance predictions, ranging from a large uptake to a release of carbon by the terrestrial biosphere, constituting a large uncertainty in the associated feedback to atmospheric CO₂ concentrations under global climate change. Errors and biases that may contribute to such uncertainty include ecosystem model structure, parameters and forcing by climate output from general circulation models (GCMs) or the atmospheric components of Earth system models (ESMs), e.g. as prepared for use in IPCC climate change assessments. The relative importance of these contributing factors to the overall uncertainty in carbon cycle projections is not well characterised. Here we investigate the role of climate model-derived biases by forcing a single global ecosystem-carbon cycle model, with original climate outputs from 15 ESMs and GCMs from the CMIP5 ensemble. We show that variation among the resulting ensemble of present and future carbon cycle simulations propagates from biases in annual means of temperature, precipitation and incoming shortwave radiation. Future changes in carbon pools, and thus land carbon sink trends, are also affected by climate biases, although to a smaller extent than the absolute size of carbon pools. Our results suggest that climate biases could be responsible for a considerable fraction of the large uncertainties in ESM simulations of land carbon fluxes and pools, amounting to about 40% of the range reported for ESMs. We conclude that climate bias-induced uncertainties must be decreased to make accurate coupled atmosphere-carbon cycle projections.

Industrial-scale oil palm cultivation is rapidly expanding in Gabon, where it has the potential to drive economic growth, but also threatens forest, biodiversity and carbon resources. The Gabonese government is promoting an ambitious agricultural expansion strategy, while simultaneously committing to minimize negative environmental impacts of oil palm agriculture. This study estimates the extent and location of suitable land for oil palm cultivation in Gabon, based on an analysis of recent trends in plantation permitting. We use the resulting suitability map to evaluate two proposed approaches to minimizing negative environmental impacts: a High Carbon Stock (HCS) approach, which emphasizes forest protection and climate change mitigation, and a High Conservation Value (HCV) approach, which focuses on safeguarding biodiversity and ecosystems. We quantify the forest area, carbon stock, and biodiversity resources protected under each approach, using newly developed maps of priority species distributions and forest biomass for Gabon. We find 2.7–3.9 Mha of suitable or moderately suitable land that avoid HCS areas, 4.4 million hectares (Mha) that avoid HCV areas, and 1.2–1.7 Mha that avoid both. This suggests that Gabon's oil palm production target could likely be met without compromising important ecosystem services, if appropriate safeguards are put in place. Our analysis improves understanding of suitability for oil palm in Gabon, determines how conservation strategies align with national targets for oil palm production, and informs national land use planning.

Feedbacks between climate warming, land surface aridity, and wildfire-derived aerosols represent a large source of uncertainty in future climate predictions. Here, long-term observations of aerosol optical depth, surface level aerosol loading, fire-area burned, and hydrologic simulations are used to show that regional-scale increases in aridity and resulting wildfires have significantly increased summertime aerosol loading in remote high elevation regions of the Intermountain West of the United States. Surface summertime organic aerosol loading and total aerosol optical depth were both strongly correlated (p < 0.05) with aridity and fire area burned at high elevation sites across major western US mountain ranges. These results demonstrate that surface-level organic aerosol loading is dominated by summertime wildfires at many high elevation sites. This analysis provides new constraints for climate projections on the influence of drought and resulting wildfires on aerosol loading. These empirical observations will help better constrain projected increases in organic aerosol loading with increased fire activity under climate change.

Tree rings have long been used to make inferences about the environmental factors that influence tree growth. Great Basin bristlecone pine is a long-lived species and valuable dendroclimatic resource, but often with mixed growth signals; in many cases, not all trees at one location are limited by the same environmental variable. Past work has identified an elevational threshold below the upper treeline above which trees are limited by temperature, and below which trees tend to be moisture limited. This study identifies a similar threshold in terms of temperature instead of elevation through fine-scale topoclimatic modeling, which uses a suite of topographic and temperature-sensor data to predict temperatures across landscapes. We sampled trees near the upper limit of growth at four high-elevation locations in the Great Basin region, USA, and used cluster analysis to find dual-signal patterns in radial growth. We observed dual-signal patterns in ring widths at two of those sites, with the signals mimicking temperature and precipitation patterns. Trees in temperature-sensitive clusters grew in colder areas, while moisture-sensitive cluster trees grew in warmer areas. We found thresholds between temperature- and moisture-sensitivity ranging from 7.4°C to 8°C growing season mean temperature. Our findings allow for a better physiological understanding of bristlecone pine growth, and seek to improve the accuracy of climate reconstructions.

Great Basin bristlecone pine (Pinus longaeva) and foxtail pine (Pinus balfouriana) are valuable paleoclimate resources due to their longevity and climatic sensitivity of their annually-resolved rings. Treeline research has shown that growing season temperatures limit tree growth at and just below the upper treeline. In the Great Basin, the presence of precisely dated remnant wood above modern treeline shows that the treeline ecotone shifts at centennial timescales tracking long-term changes in climate; in some areas during the Holocene climatic optimum treeline was 100 meters higher than at present. Regional treeline position models built exclusively from climate data may identify characteristics specific to Great Basin treelines and inform future physiological studies, providing a measure of climate sensitivity specific to bristlecone and foxtail pine treelines. This study implements a topoclimatic analysis—using topographic variables to explain patterns in surface temperatures across diverse mountainous terrain—to model the treeline position of three semi-arid bristlecone and/or foxtail pine treelines in the Great Basin as a function of growing season length and mean temperature calculated from in situ measurements. Results indicate: (1) the treeline sites used in this study are similar to other treelines globally, and require a growing season length of between 147–153 days and average temperature ranging from 5.5°C–7.2°C, (2) site-specific treeline position models may be improved through topoclimatic analysis and (3) treeline position in the Great Basin is likely out of equilibrium with the current climate, indicating a possible future upslope shift in treeline position.

The feedbacks between climate, atmospheric CO₂ concentration and the terrestrial carbon cycle are a major source of uncertainty in future climate projections with Earth systems models. Here, we use observation-based estimates of the interannual variations in evapotranspiration (ET), net biome productivity (NBP), as well as the present-day sensitivity of NBP to climate variations, to constrain globally the terrestrial carbon cycle feedbacks as simulated by models that participated in the fifth phase of the coupled model intercomparison project (CMIP5). The constraints result in a ca. 40% lower response of NBP to climate change and a ca. 30% reduction in the strength of the CO₂ fertilization effect relative to the unconstrained multi-model mean. While the unconstrained CMIP5 models suggest an increase in the cumulative terrestrial carbon storage (477 PgC) in response to an idealized scenario of 1%/year atmospheric CO₂ increase, the constraints imply a ca. 19% smaller change. Overall, the applied emerging constraint approach offers a possibility to reduce uncertainties in the projections of the terrestrial carbon cycle, which is a key determinant of the future trajectory of atmospheric CO₂ concentration and resulting climate change.

Disruptions to transportation networks by natural hazard events cause direct losses (e.g. by physical damage) and indirect socio-economic losses via travel delays and decreased transportation efficiency. The severity and spatial distribution of these losses varies according to user travel demands and which links, nodes or infrastructure assets are physically disrupted. Increasing transport network resilience, for example by targeted mitigation strategies, requires the identification of the critical network segments which if disrupted would incur undesirable or unacceptable socio-economic impacts. Here, these impacts are assessed on a national road transportation network by coupling hazard data with a transport network model. This process is illustrated using a case study of landslide hazards on the road network of Scotland. A set of possible landslide-prone road segments is generated using landslide susceptibility data. The results indicate that at least 152 road segments are susceptible to landslides, which could cause indirect economic losses exceeding £35 k for each day of closure. In addition, previous estimates for historic landslide events might be significant underestimates. For example, the estimated losses for the 2007 A83 'Rest and Be Thankful' landslide are £80 k day⁻¹, totalling £1.2 million over a 15 day closure, and are ∼60% greater than previous estimates. The spatial distribution of impact to road users is communicated in terms of 'extended hazard impact footprints'. These footprints reveal previously unknown exposed communities and unanticipated spatial patterns of severe disruption. Beyond cost-benefit analyses for landslide mitigation efforts, the approach implemented is applicable to other natural hazards (e.g. flooding), combinations of hazards, or even other network disruption events.

Evapotranspiration (ET) is a key process of the climate system because it links water, energy and carbon cycles. In this study we modified a Penman–Monteith based algorithm to estimate ET on the Tibetan Plateau at a 1 km spatial resolution for the period 2000–2010 using meteorological and satellite remote sensing data. The results showed that the average annual ET on the Tibetan Plateau was 350.3 mm year⁻¹ and decreased from the southeast toward the northwest. The highest ET value was found in open water bodies (680.9 mm year⁻¹) and the lowest ET value was found in open shrubland (254.0 mm year⁻¹). Overall, the inter-annual ET decreased from 2000 to 2010 and there was significant negative ET trend over 42% of the region, primarily in the northwest of the Tibetan Plateau. Relative humidity was the dominant factor in controlling long-term variations of ET in the arid northwest plateau. But under moist conditions, leaf area index or temperature drove ET. In addition, P-ET on the Tibetan Plateau significantly increased and about 37% of the region showed strong positive P-ET trend primarily in the central of the Tibetan Plateau. The positive P-ET trend in four seasons suggested that the Tibetan Plateau might have become wetter during the past decade.

Despite progresses in climate change science, projections of future sea-level rise remain highly uncertain, especially due to large unknowns in the melting processes affecting the ice-sheets in Greenland and Antarctica. Based on climate-models outcomes and the expertise of scientists concerned with these issues, the IPCC provided constraints to the quantiles of sea-level projections. Moreover, additional physical limits to future sea-level rise have been established, although approximately. However, many probability functions can comply with this imprecise knowledge. In this contribution, we provide a framework based on extra-probabilistic theories (namely the possibility theory) to model the uncertainties in sea-level rise projections by 2100 under the RCP 8.5 scenario. The results provide a concise representation of uncertainties in future sea-level rise and of their intrinsically imprecise nature, including a maximum bound of the total uncertainty. Today, coastal impact studies are increasingly moving away from deterministic sea-level projections, which underestimate the expectancy of damages and adaptation needs compared to probabilistic laws. However, we show that the probability functions used so-far have only explored a rather conservative subset of sea-level projections compliant with the IPCC. As a consequence, coastal impact studies relying on these probabilistic sea-level projections are expected to underestimate the possibility of large damages and adaptation needs.

Temperature data are commonly used to estimate the sensitivity of many societally relevant outcomes, including crop yields, mortality, and economic output, to ongoing climate changes. In many tropical regions, however, temperature measures are often very sparse and unreliable, limiting our ability to understand climate change impacts. Here we evaluate satellite measures of near-surface temperature (Ts) as an alternative to traditional air temperatures (Ta) from weather stations, and in particular their ability to replace Ta in econometric estimation of climate response functions. We show that for maize yields in Africa and the United States, and for economic output in the United States, regressions that use Ts produce very similar results to those using Ta, despite the fact that daily correlation between the two temperature measures is often low. Moreover, for regions such as Africa with poor station coverage, we find that models with Ts outperform models with Ta, as measured by both R² values and out-of-sample prediction error. The results indicate that Ts can be used to study climate impacts in areas with limited station data, and should enable faster progress in assessing risks and adaptation needs in these regions.

The future trajectory of crop yields in the United States will influence food supply and land use worldwide. We examine maize and soybean yields for 2000–2015 in the Midwestern U.S. using a new satellite-based dataset on crop yields at 30m resolution. We quantify heterogeneity both within and between fields, and find that the difference between average and top yielding fields is typically below 30% for both maize and soybean, as expected in advanced agricultural regions. In most counties, within-field heterogeneity is at least half as large as overall heterogeneity, illustrating the importance of non-management factors such as soil and landscape position. Surprisingly, we find that yield heterogeneity is rising in maize, both between and within fields, with average yield differences between the best and worst soils more than doubling since 2000. Heterogeneity trends were insignificant for soybean. The findings are consistent both with recent adoption of precision agriculture technologies and with recent trends toward denser sowing in maize, which disproportionately raise yields on better soils. The results imply that yield gains in the region are increasingly derived from the more productive land, and that sub-field precision management of nutrients and other inputs is increasingly warranted.

Revenues derived from carbon have been seen as an important tool for supporting forest conservation over the past decade. At the same time, there is high uncertainty about how much revenue can reasonably be expected from land use emissions reductions initiatives. Despite this uncertainty, REDD+ projects and conservation initiatives that aim to take advantage of available or, more commonly, future funding from carbon markets have proliferated. This study used participatory multi-stakeholder workshops to develop divergent future scenarios of land use in eight landscapes in four countries around the world: Peru, Indonesia, Tanzania, and Mexico. The results of these future scenario building exercises were analyzed using a new tool, CarboScen, for calculating the landscape carbon storage implications of different future land use scenarios. The findings suggest that potential revenues from carbon storage or emissions reductions are significant in some landscapes (most notably the peat forests of Indonesia), and much less significant in others (such as the low-carbon forests of Zanzibar and the interior of Tanzania). The findings call into question the practicality of many conservation programs that hinge on expectations of future revenue from carbon finance. The future scenarios-based approach is useful to policy-makers and conservation program developers in distinguishing between landscapes where carbon finance can substantially support conservation, and landscapes where other strategies for conservation and land use should be prioritized.

Drought can affect the structure, composition and function of terrestrial ecosystems, yet drought impacts and post-drought recovery potentials of different land cover types have not been extensively studied at a global scale. We evaluated drought impacts on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) of different global terrestrial ecosystems, as well as the drought-resilience of each ecosystem type during the period of 2000 to 2011. Using GPP as biome vitality indicator against drought stress, we developed a model to examine ecosystem resilience represented by the length of recovery days (LRD). LRD presented an evident gradient of high (>60 days) in mid-latitude region and low (<60 days) in low (tropical area) and high (boreal area) latitude regions. As average GPP increased, the LRD showed a significantly decreasing trend, indicating readiness to recover after drought, across various land cover types (R² = 0.68, p < 0.0001). Moreover, zonal analysis revealed that the most dramatic reduction of the drought-induced GPP was found in the mid-latitude region of the Northern Hemisphere (48% reduction), followed by the low-latitude region of the Southern Hemisphere (13% reduction). In contrast, a slightly enhanced GPP (10%) was evident in the tropical region under drought impact. Additionally, the highest drought-induced reduction of ET was found in the Mediterranean area, followed by Africa. Water use efficiency, however, showed a pattern of decreasing in the Northern Hemisphere and increasing in the Southern Hemisphere. Drought induced reductions of WUE ranged from 0.96% to 27.67% in most of the land cover types, while the increases of WUE found in Evergreen Broadleaf Forest and savanna were about 7.09% and 9.88%, respectively. These increases of GPP and WUE detected during drought periods could either be due to water-stress induced responses or data uncertainties, which require further investigation.

A quantification of present and future mean annual losses due to extreme coastal events can be crucial for adequate decision making on adaptation to climate change in coastal areas around the globe. However, this approach is limited when uncertainty needs to be accounted for. In this paper, we assess coastal flood risk from sea-level rise and extreme events in 120 major cities around the world using an alternative stochastic approach that accounts for uncertainty. Probability distributions of future relative (local) sea-level rise have been used for each city, under three IPPC emission scenarios, RCP 2.6, 4.5 and 8.5. The approach allows a continuous stochastic function to be built to assess yearly evolution of damages from 2030 to 2100. Additionally, we present two risk measures that put low-probability, high-damage events in the spotlight: the Value at Risk (VaR) and the Expected Shortfall (ES), which enable the damages to be estimated when a certain risk level is exceeded. This level of acceptable risk can be defined involving different stakeholders to guide progressive adaptation strategies. The method presented here is new in the field of economics of adaptation and offers a much broader picture of the challenges related to dealing with climate impacts. Furthermore, it can be applied to assess not only adaptation needs but also to put adaptation into a timeframe in each city.

Ecosystem carbon (C) uptake in terrestrial ecosystems has increased over the past five decades, but with large interannual variability (IAV). However, we are not clear on the attribution and the processes that control the long-term trend and IAV of land C uptake. Using atmospheric inversion net ecosystem exchange (NEE) data, we quantified the trend and IAV of NEE across the globe, the Northern Hemisphere (NH), and the Southern Hemisphere (SH), and decomposed NEE into carbon uptake amplitude and duration during each year from 1979–2013. We found the NH rather than the SH determined the IAV, while both hemispheres contributed equivalently to the global NEE trend. Different ecosystems in the NH and SH had differential relative contributions to their trend and IAV. The long-term trends of increased C uptake across the globe and the SH were attributed to both extended duration and increasing amplitude of C uptake. The shortened duration of uptake in the NH partly offsets the effects of increased NEE amplitude, making the net C uptake trend the same as that of the SH. The change in NEE IAV was also linked to changes in the amplitude and duration of uptake, but they worked in different ways in the NH, SH and globe. The fundamental attributions of amplitude and duration of C uptake revealed in this study are helpful to better understand the mechanisms underlying the trend and IAV of land C uptake. Our findings also suggest the critical roles of grassland and croplands in the NH in contributing to the trend and IAV of land C uptake.

In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from weather@home with a regional version of HadAM3P. These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles. However, the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the magnitude in the atmosphere-only model ensembles due to larger ensemble sizes from single models with more constrained simulations. Certainty is greatly decreased when considering a CMIP5 ensemble that can represent the relevant teleconnections due to a decrease in ensemble members. An increase in probability of high precipitation in HadGEM3-A using the observed trend in sea surface temperatures (SSTs) for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect.

The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO₂), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO₂, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO₂ increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO₂ effects into statistical studies, improving both approaches' treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally requiring fewer resources to produce robust estimates, especially when applied to crops beyond the major grains.

Previous studies have shown that global mean surface air temperature remains elevated after cessation of CO₂ emissions. However, studies differ in whether the temperature continues to increase, slowly decreases, or remains constant after cessation of emissions. An understanding of this committed warming is of importance because it has implication for the estimation of carbon budgets compatible with temperature targets. Here, we investigate the effect of the state of thermal and bio-geochemical equilibration at the time emissions are set to zero on the committed warming as the latter is determined by the balance of these two equilibration processes. We find that the effect of thermal equilibration, expressed as fraction of realized warming, dominates over the bio-geochemical equilibration, expressed as ratio of the airborne fraction to the equilibrium airborne fraction. This leads to a positive warming commitment, and a commitment that declines the later emissions are zeroed along a trajectory of constant atmospheric CO₂ concentration. We furthermore show that the scenario prior to zeroed emissions has the strongest effect on the warming commitment, compared to the time of zeroed emissions and the time horizon over which the commitment is calculated.

Futures contracts are exchange-traded financial instruments that enable parties to fix a price in advance, for later performance on a contract. Forward contracts also entail future settlement, but they are traded directly between two parties. Futures and forwards are used in commodities trading, as producers seek financial security when planning production. We discuss the potential use of futures contracts in Carbon Dioxide Removal (CDR) markets; concluding that they have one principal advantage (near-term price security to current polluters), and one principal disadvantage (a combination of high price volatility and high trade volume means contracts issued by the private sector may cause systemic economic risk). Accordingly, we note the potential for the development of futures markets in CDR, but urge caution about the prospects for market failure. In particular, we consider the use of regulated markets: to ensure contracts are more reliable, and that moral hazard is minimised. While regulation offers increased assurances, we identify major insufficiencies with this approach—finding it generally inadequate. In conclusion, we suggest that only governments can realistically support long-term CDR futures markets. We note existing long-term CDR plans by governments, and suggest the use of state-backed futures for supporting these assurances.