Jonathan C. Doelman

Hotspots of uncertainty in land use and land cover change projections: a global scale model comparison

Abstract Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.

Scenarios towards limiting global mean temperature increase below 1.5 °C

The 2015 Paris Agreement calls for countries to pursue efforts to limit global-mean temperature rise to 1.5 °C. The transition pathways that can meet such a target have not, however, been extensively explored. Here we describe scenarios that limit end-of-century radiative forcing to 1.9 W m−2, and consequently restrict median warming in the year 2100 to below 1.5 °C. We use six integrated assessment models and a simple climate model, under different socio-economic, technological and resource assumptions from five Shared Socio-economic Pathways (SSPs). Some, but not all, SSPs are amenable to pathways to 1.5 °C. Successful 1.9 W m−2 scenarios are characterized by a rapid shift away from traditional fossil-fuel use towards large-scale low-carbon energy supplies, reduced energy use, and carbon-dioxide removal. However, 1.9 W m−2 scenarios could not be achieved in several models under SSPs with strong inequalities, high baseline fossil-fuel use, or scattered short-term climate policy. Further research can help policy-makers to understand the real-world implications of these scenarios.Scenarios that constrain end-of-century radiative forcing to 1.9 W m–2, and thus global mean temperature increases to below 1.5 °C, are explored. Effective scenarios reduce energy use, deploy CO2 removal measures, and shift to non-emitting energy sources.

Assessing uncertainties in land cover projections

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

Alternative pathways to the 1.5 °C target reduce the need for negative emission technologies

Mitigation scenarios that achieve the ambitious targets included in the Paris Agreement typically rely on greenhouse gas emission reductions combined with net carbon dioxide removal (CDR) from the atmosphere, mostly accomplished through large-scale application of bioenergy with carbon capture and storage, and afforestation. However, CDR strategies face several difficulties such as reliance on underground CO2 storage and competition for land with food production and biodiversity protection. The question arises whether alternative deep mitigation pathways exist. Here, using an integrated assessment model, we explore the impact of alternative pathways that include lifestyle change, additional reduction of non-CO2 greenhouse gases and more rapid electrification of energy demand based on renewable energy. Although these alternatives also face specific difficulties, they are found to significantly reduce the need for CDR, but not fully eliminate it. The alternatives offer a means to diversify transition pathways to meet the Paris Agreement targets, while simultaneously benefiting other sustainability goals.Scenarios that constrain warming to 1.5 °C currently place a large emphasis on CO2 removal. Alternative pathways involving lifestyle change, rapid electrification and reduction of non-CO2 gases could reduce the need for such negative emission technologies.

Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy.

Biogeophysical Impacts of Land‐Use Change on Climate Extremes in Low‐Emission Scenarios: Results From HAPPI‐Land

Abstract The impacts of land use have been shown to have considerable influence on regional climate. With the recent international commitment to limit global warming to well below 2°C, emission reductions need to be ambitious and could involve major land‐use change (LUC). Land‐based mitigation efforts to curb emissions growth include increasing terrestrial carbon sequestration through reforestation, or the adoption of bioenergy crops. These activities influence local climate through biogeophysical feedbacks, however, it is uncertain how important they are for a 1.5° climate target. This was the motivation for HAPPI‐Land: the half a degree additional warming, prognosis, and projected impacts—land‐use scenario experiment. Using four Earth system models, we present the first multimodel results from HAPPI‐Land and demonstrate the critical role of land use for understanding the characteristics of regional climate extremes in low‐emission scenarios. In particular, our results show that changes in temperature extremes due to LUC are comparable in magnitude to changes arising from half a degree of global warming. We also demonstrate that LUC contributes to more than 20% of the change in temperature extremes for large land areas concentrated over the Northern Hemisphere. However, we also identify sources of uncertainty that influence the multimodel consensus of our results including how LUC is implemented and the corresponding biogeophysical feedbacks that perturb climate. Therefore, our results highlight the urgent need to resolve the challenges in implementing LUC across models to quantify the impacts and consider how LUC contributes to regional changes in extremes associated with sustainable development pathways.

Risk of increased food insecurity under stringent global climate change mitigation policy

Food insecurity can be directly exacerbated by climate change due to crop-production-related impacts of warmer and drier conditions that are expected in important agricultural regions1–3. However, efforts to mitigate climate change through comprehensive, economy-wide GHG emissions reductions may also negatively affect food security, due to indirect impacts on prices and supplies of key agricultural commodities4–6. Here we conduct a multiple model assessment on the combined effects of climate change and climate mitigation efforts on agricultural commodity prices, dietary energy availability and the population at risk of hunger. A robust finding is that by 2050, stringent climate mitigation policy, if implemented evenly across all sectors and regions, would have a greater negative impact on global hunger and food consumption than the direct impacts of climate change. The negative impacts would be most prevalent in vulnerable, low-income regions such as sub-Saharan Africa and South Asia, where food security problems are already acute.Economy-wide GHG emissions reductions may negatively affect food security. Stringent mitigation policies, modelled as carbon prices, are shown to lead to an increase in production costs, food prices and the population’s risk of hunger.

Greenhouse gas emission curves for advanced biofuel supply chains

Most climate change mitigation scenarios that are consistent with the 1.5–2 °C target rely on a large-scale contribution from biomass, including advanced (second-generation) biofuels. However, land-based biofuel production has been associated with substantial land-use change emissions. Previous studies show a wide range of emission factors, often hiding the influence of spatial heterogeneity. Here we introduce a spatially explicit method for assessing the supply of advanced biofuels at different emission factors and present the results as emission curves. Dedicated crops grown on grasslands, savannahs and abandoned agricultural lands could provide 30 EJBiofuel yr−1 with emission factors less than 40 kg of CO2-equivalent (CO2e) emissions per GJBiofuel (for an 85-year time horizon). This increases to 100 EJBiofuel yr−1 for emission factors less than 60 kgCO2e GJBiofuel−1. While these results are uncertain and depend on model assumptions (including time horizon, spatial resolution, technology assumptions and so on), emission curves improve our understanding of the relationship between biofuel supply and its potential contribution to climate change mitigation while accounting for spatial heterogeneity.Here emission curves are developed for advanced biofuel supply chains to enhance understanding of the relationship between biofuel supply and its potential contribution to climate change mitigation while accounting for landscape heterogeneity.

A Global Analysis of Future Water Deficit Based On Different Allocation Mechanisms

Freshwater scarcity is already an urgent problem in some areas but may increase significantly in the future. To assess future developments, we need to understand how future population growth, agricultural production patterns, energy use, economic development, and climate change may impact the global freshwater cycle. Integrated models provide opportunities for quantitative assessment. In this paper, we further integrate models of hydrology and economics, using the models IMAGE and LPJmL, with explicit accounting for (1) electricity, industry, and municipal and irrigation water use; (2) intersectoral water allocation rules at the 0.5° × 0.5°grid scale; and (3) withdrawal, consumption, and return flows. With the integration between hydrology and economy we are able to understand competition dynamics between the different freshwater users at the basin and grid scale. We run model projections for three Shared Socioeconomic Pathways (SSPs), more efficient water use, and no expansion of irrigated areas to understand the competition dynamics of these different allocation mechanisms. We conclude that (1) global water withdrawal is projected to increase by 12% in SSP-1, 26% in SSP-2, and 29% in SSP-3 during 2010–2050; (2) water deficits (demand minus allocated water) for nonagricultural uses are small in 2010 but become significant around 2050; (3) interannual variability of precipitation results in variability of water deficits; (4) water use efficiency improvements reduce water withdrawal but have little impact on water deficits; and (5) priority rules at the local level have a large effect on water deficits, whereas limiting the expansion of irrigation has virtually no effect.

Towards pathways bending the curve terrestrial biodiversity trends within the 21st century

Unless actions are taken to reduce multiple anthropogenic pressures, biodiversity is expected to continue declining at an alarming rate. Models and scenarios can be used to help design the pathways to sustain a thriving nature and its ability to contribute to people. This approach has so far been hampered by the complexity associated with combining projections of pressures on, and subsequent responses from, biodiversity. Most previous assessments have projected continuous biodiversity declines and very few have identified pathways for reversing the loss of biodiversity without jeopardizing other objectives such as development or climate mitigation. The Bending The Curve initiative set out to advance quantitative modelling techniques towards ambitious scenarios for biodiversity. In this proof-of-concept analysis, we developed a modelling approach that demonstrates how global land use and biodiversity models can shed light on wedges able to bend the curve of biodiversity trends as affected by land-use change, the biggest current threat to biodiversity. In order to address the uncertainties associated with such pathways we used a multi-model framework and relied on the Shared Socioeconomic Pathway/Representative Concentration Pathway scenario framework. This report describes the details of this modelling approach.