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Projection of land use and land cover change is highly uncertain yet drives critical estimates of carbon emissions, climate change, and food and bioenergy production. We use new, spatially-explicit land availability data in conjunction with a model sensitivity analysis to estimate the effects of additional land protection on land use and cover. The land availability data include protected land and agricultural suitability and are incorporated into the Moirai land data system for initializing the Global Change Analysis Model (GCAM). Overall, decreasing land availability is relatively inefficient at preserving undeveloped land while having considerable regional land use impacts. Current amounts of protected area have little effect on land and crop production estimates, but including the spatial distribution of unsuitable (i.e., unavailable) land dramatically shifts bioenergy production from high northern latitudes to the rest of the world, as compared to uniform availability. This highlights the importance of spatial heterogeneity in understanding and managing land change. Approximately doubling current protected area to emulate a 30% protected area target may avoid land conversion by 2050 of less than half the newly protected extent while reducing bioenergy feedstock land by 10.4% and cropland and grazed pasture by over 3%. Regional bioenergy land may be reduced (increased) by up to 46% (36%), cropland reduced by up to 61%, pasture reduced by up to 100%, and harvested forest reduced by up to 35%. Only a few regions show notable gains in some undeveloped land types of up to 36%. Half of the regions can reach the target using only unsuitable land, which would minimize impacts to agriculture but may not meet conservation goals.A.V. Di Vittorio, K.B. Narayan, P. Patel, K. Calvin, C.R. Vernon (2022). Doubling protected land area may be inefficient at preserving the extent of undeveloped land and could cause substantial regional shifts in land use. GCB Bioenergy, 00:1-23. http://doi.org/10.1111/gcbb.13

The representations of international trade and global market integration play central roles in long-term global agricultural economic modeling. However, the conventional use of the gravity models (i.e., the Armington approach) of trade may not adequately account for the dynamic process of market integration. In this paper, we generalize a logit-based Armington approach by permitting home preference bias erosion to account for a trend of global market integration. We build a simple agricultural economic equilibrium model and conduct hindcast experiments to examine the historical trend of market integration. The results show a significant integrating trend of the global agricultural market implied by converging Armington preference distributions (i.e., home bias erosion) in 1995–2015. We demonstrate important implications of allowing the dynamic trend of market integration implied by bias erosion on estimating trade elasticities. By testing the future market integration trend implied by historical home bias integration in the Global Change Analysis Model (GCAM), we show a high sensitivity of long-term agroeconomic projections to future trade and market integration scenarios.

Hurtt, George C., Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, et al. 2020. ‘Harmonization of Global Land Use Change and Management for the Period 850–2100 (LUH2) for CMIP6’. Geoscientific Model Development 13 (11): 5425–64. https://doi.org/10.5194/gmd-13-5425-2020.

The maximum future projected bioenergy expansion potential, in scenarios limiting warming to 2°C or below, is equivalent to half of present-day croplands. We quantify the impacts of large-scale bioenergy expansion against re/afforestation, which remain elusive, using an integrated human-natural system modeling framework with explicit representation of perennial bioenergy crops. The end-of-century net carbon sequestration due to bioenergy deployment coupled with carbon capture and storage largely depends on fossil fuel displacement types, ranging from 11.4 to 31.2 PgC over the conterminous United States. These net carbon sequestration benefits are inclusive of a 10 PgC carbon release due to land use conversions and a 2.4 PgC loss of additional carbon sink capacity associated with bioenergy-driven deforestation. Moreover, nearly one-fourth of U.S. land areas will suffer severe water stress by 2100 due to either reduced availability or deteriorated quality. These broader impacts of bioenergy expansion should be weighed against the costs and benefits of re/afforestation-based strategies.

Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05° × 0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts., Measurement(s) land use process • global land use and land cover Technology Type(s) computational modeling technique Factor Type(s) year of data collection • Shared Socioeconomic Pathways scenarios • Representative Concentration Pathways scenarios Sample Characteristic - Environment land Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12967760

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.

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions.

We present the analysis of global sympagic primary production (PP) from 300 years of pre-industrial and historical simulations of the E3SMv1.1-BGC model. The model includes a novel, eight-element sea ice biogeochemical component, MPAS-Seaice zbgc, which is resolved in three spatial dimensions and uses a vertical transport scheme based on internal brine dynamics. Modeled ice algal chlorophyll-a concentrations and column-integrated values are broadly consistent with observations, though chl-a profile fractions indicate that upper ice communities of the Southern Ocean are underestimated. Simulations of polar integrated sea ice PP support the lower bound in published estimates for both polar regions with mean Arctic values of 7.5 and 15.5 TgC/a in the Southern Ocean. However, comparisons of the polar climate state with observations, using a maximal bound for ice algal growth rates, suggest that the Arctic lower bound is a significant underestimation driven by biases in ocean surface nitrate, and that correction of these biases supports as much as 60.7 TgC/a of net Arctic PP. Simulated Southern Ocean sympagic PP is predominantly light-limited, and regional patterns, particularly in the coastal high production band, are found to be negatively correlated with snow thickness.

Stabilizing climate change well below 2 °C and towards 1.5 °C requires comprehensive mitigation of all greenhouse gases (GHG), including both CO2 and non-CO2 GHG emissions. Here we incorporate the latest global non-CO2 emissions and mitigation data into a state-of-the-art integrated assessment model GCAM and examine 90 mitigation scenarios pairing different levels of CO2 and non-CO2 GHG abatement pathways. We estimate that when non-CO2 mitigation contributions are not fully implemented, the timing of net-zero CO2 must occur about two decades earlier. Conversely, comprehensive GHG abatement that fully integrates non-CO2 mitigation measures in addition to a net-zero CO2 commitment can help achieve 1.5 °C stabilization. While decarbonization-driven fuel switching mainly reduces non-CO2 emissions from fuel extraction and end use, targeted non-CO2 mitigation measures can significantly reduce fluorinated gas emissions from industrial processes and cooling sectors. Our integrated modeling provides direct insights in how system-wide all GHG mitigation can affect the timing of net-zero CO2 for 1.5 °C and 2 °C climate change scenarios., Stabilizing climate change requires simultaneous mitigation of all greenhouse gas emissions (GHGs). Here the authors examine 90 mitigation scenarios pairing different levels of CO2 and non-CO2 GHG abatement pathways to demonstrate the contributions of different GHGs towards 1.5 °C and 2 °C goals.

Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) does not represent perennial bioenergy crops, however. In this study, we expand ELM’s crop model to include perennial bioenergy crops whose production increases in modeled socioeconomic pathways owing to their potential for mitigating climate change. We focus on high-productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies eight parameters associated with phenology, carbon/nitrogen allocation, and photosynthetic capacity as the most sensitive parameters for carbon and energy fluxes. We calibrated the model against observations collected at the University of Illinois Energy Farm for carbon and energy fluxes, and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The model accurately represents the seasonality of energy fluxes, but their magnitude is not well captured. This work provides a foundation for future analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger earth system and can also be used for studying the impact of future biofuel expansion on climate and terrestrial systems.

This paper describes GCAM-USA v5.3_water_dispatch, an open source model that represents key interactions across economic, energy, water, and land systems in a consistent global framework, with subnational detail in the United States. GCAM-USA divides the world into 31 geopolitical regions outside the United States (U.S.) and represents the U.S. economic and energy systems in 51 state-level regions (50 states plus the District of Columbia). The model also includes 235 water basins and 384 land-use regions; 23 of each fall at least partially within the United States. GCAM-USA offers a level of process and temporal resolution rare for models of its class and scope, including detailed subnational representation of U.S. water demands and supplies and sub-annual operations (day/night for each month) in the U.S. electric power sector. GCAM-USA can be used to explore how changes in socioeconomic drivers, technological progress, or policy impact demands for, and production of, energy, water, and crops at a subnational level in the United States, while maintaining consistency with broader national and international conditions. This paper describes GCAM-USA’s structure, inputs, and outputs, with emphasis on new model features. Four illustrative scenarios encompassing varying socioeconomic and energy system futures are used to explore subnational changes in energy, water, and land-use outcomes. We conclude with information about how public users can access the model.

The human and Earth systems are intricately linked: Climate influences agricultural production, renewable energy potential, and water availability, for example, while anthropogenic emissions from industry and land use change alter temperature and precipitation. Such feedbacks have the potential to significantly alter future climate change. These feedbacks may also exert significant changes on 21st-century energy, agriculture, land use and carbon cycle projections, but little is known about their possible magnitudes, or about regional and sector dynamics under different forcing scenarios. Here we use an integrated Earth System Model (ESM) featuring bidirectional information exchange between an economically-oriented integrated assessment model and the ESM to examine how human-natural feedbacks operate under high and medium radiative forcing (RF) scenarios. Specifically, we examine the effect of changing land productivity on human systems, and the effect of changing land use/land cover and CO2 emissions on the Earth system. We find that the effect of coupling differs across radiative forcing levels and across regions, due to differences in the climate signal, human responses to those signals, and regional characteristics. In particular, we find reductions in cropland area due to feedbacks in both the medium and high RF scenarios. In the medium RF scenario, these reductions result in increased area for bioenergy and afforestation and reduced energy system CO2 emissions, as the carbon price in this scenario incentivizes low carbon energy sources and terrestrial carbon storage; these incentives are absent in the high RF scenario. These differences are key to understanding the possible future evolution pathways of the integrated Earth system in response to 21st century climate change. Additional models and hypothesis testing are needed to determine exactly when and how bidirectional feedbacks between human and Earth systems should be considered in future assessments.

Many of the world's major freshwater aquifers are being exploited unsustainably, with some projected to approach environmentally unsafe drawdown limits within the 21st century. Given that aquifer depletion tends to occur in important crop producing regions, the prospect of running dry poses a significant threat to global food security. Here we use the Global Change Assessment Model (GCAM) to explore the response of land use and agriculture sectors to severe constraints on global water resources. We simulate a scenario in which a number of important groundwater aquifers become depleted to the point where further water withdrawal is unviable, either due to excessive extraction costs or environmental limits being reached. Results are then benchmarked against a scenario that neglects constraints on water withdrawals. We find that groundwater depletion and associated water price increases drive two distinct responses in the agriculture sector: an expansion of rain fed agriculture, and a shift in irrigated crop production toward regions with cheaper water resources. Losses in crop production are most pronounced in water stressed regions where groundwater is being depleted unsustainably to meet irrigation demands-namely northwest India, Pakistan, the Middle East, western United States, Mexico, and Central Asia. While these results highlight substantial risks for the affected regional agricultural economies, we show that modest changes in irrigation and location of crop growth, in a world with frictionless trade, could ensure global food demands are met despite severe water constraints.

Agriculture accounts for 90% of global freshwater consumption and it is expected to intensify in the future. Climate and land use changes are two major factors affecting crop green and blue water consumption, and in this study we explicitly consider the effects of both factors in a consistent modeling framework. Two important research questions are addressed: 1) How will global crop green and blue water consumption evolve over the 21st century under climate and land use changes; and 2) what are the individual and combined effects of climate and land use changes on future crop green and blue water consumption? To tackle these two questions, a crop water use module is developed based on the Global Change Assessment Model (GCAM) and its hydrology module (i.e., Xanthos). Crop specific green and blue water consumption are then calculated at global 0.5° × 0.5° grid scale. Results show that global crop green water consumption increases by 12% in 2090s when compared with that in 1971–2000, and climate change dominates over land use change in determining the trend of global crop green water consumption. However, expansion in global irrigated area dominates the changing trend of global crop blue water consumption which increases 70% by 2090s, especially in regions with significant irrigated land expansion (e.g. northern Africa, central Asia, China, Mexico, the Middle East, Russia, southern Asia, and Argentina). Furthermore, global crop blue water dependence will increase under climate and land use changes, especially in arid regions.

Demeter is a community spatial downscaling model that disaggregates land use and land cover changes projected by integrated human–Earth system models. Demeter has not been intensively calibrated, and we still lack good knowledge about its sensitivity to key parameters and parameter uncertainties. We used long-term global satellite-based land cover records to calibrate key Demeter parameters. The results identified the optimal parameter values and showed that the parameterization substantially improved the model's performance. The parameters of intensification ratio and selection threshold were the most sensitive and needed to be carefully tuned, especially for regional applications. Further, small parameter uncertainties after calibration can be inflated when propagated into future scenarios, suggesting that users should consider the parameterization equifinality to better account for the uncertainties in Demeter-downscaled products. Our study provides a key reference for Demeter users and ultimately contributes to reducing the uncertainties in Earth system model simulations.

Future changes in Earth system state will impact agricultural yields and, through these changed yields, can have profound impacts on the global economy. Global gridded crop models estimate the influence of these Earth system changes on future crop yields but are often too computationally intensive to dynamically couple into global multi-sector economic models, such as the Global Change Assessment Model (GCAM) and other similar-in-scale models. Yet, generalizing a faster site-specific crop model's results to be used globally will introduce inaccuracies, and the question of which model to use is unclear given the wide variation in yield response across crop models. To examine the feedback loop among socioeconomics, Earth system changes, and crop yield changes, rapidly generated yield responses with some quantification of crop response uncertainty are desirable. The Persephone v1.0 response functions presented in this work are based on the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Climate-Crop Modeling Project (C3MP) sensitivity test data set and are focused on providing GCAM and similar models with a tractable number of rapid to evaluate dynamic yield response functions corresponding to a range of the yield response sensitivities seen in the C3MP data set. With the Persephone response functions, a new variety of agricultural impact experiments will be open to GCAM and other economic models: for example, examining the economic impacts of a multi-year drought in a key agricultural region and how economic changes in response to the drought can, in turn, impact the drought.