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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Modulation of regional carbon uptake by AMOC and alkalinity changes in the subpolar North Atlantic under a warming climate

The slowdown of the Atlantic Meridional Overturning Circulation (AMOC) and associated consequences on ocean carbon uptake could have large implications for the Earth's climate system and its global carbon cycle. This study analyzes ten Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reveals that the regional carbon uptake in the subpolar North Atlantic under a high CO 2 emission scenario moderately correlates with the decline in AMOC at 40°N. AMOC transports warm and salty subtropical waters to the subpolar regions. Models with stronger AMOC slowdown generally exhibit weaker surface warming and larger decline of surface salinity and alkalinity. We consider two plausible mechanisms linking the AMOC slowdown to the decline of regional CO 2 uptake: the reduction in surface alkalinity and diminished subduction. The decline of surface salinity and alkalinity reduces the ocean's capacity to buffer acids leading to a reduced CO 2 uptake. This important contribution is unique to the North Atlantic. Diminished convective mixing and subduction of surface water can further decrease the downward transport of anthropogenic carbon, as also shown in previous research. The centennial trends of p CO 2 are decomposed into four components driven by temperature, salinity, alkalinity and dissolved inorganic carbon, revealing that alkalinity and dissolved inorganic carbon are both significant contributors. The alkalinity-driven p CO 2 essentially follows surface salinity, establishing the linkage between AMOC slowdown and alkalinity decline. Our results indicate that alkalinity changes are important for the interplay between AMOC and the regional carbon sequestration ability across the late 20 th and the entirety of the 21 st century in the subpolar North Atlantic.

AMOC↗
DOE OSTI2023

Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean

Abstract Two coupled climate models, differing primarily in horizontal resolution and treatment of mesoscale eddies, were used to assess the impact of perturbations in wind stress and Antarctic ice sheet (AIS) melting on the Southern Ocean meridional overturning circulation (SO MOC), which plays an important role in global climate regulation. The largest impact is found in the SO MOC lower limb, associated with the formation of Antarctic Bottom Water (AABW), which in both models is enhanced by wind and weakened by AIS meltwater perturbations. Even though both models under the AIS melting perturbation show similar AABW transport reductions of 4–5 Sv (50%–60%), the volume deflation of AABW south of 30°S is four times greater in the higher resolution simulation (−20 vs. −5 Sv). Water mass transformation (WMT) analysis reveals that surface‐forced dense water formation on the Antarctic shelf is absent in the higher resolution and reduced by half in the lower resolution model in response to the increased AIS melting. However, the decline of the AABW volume (and its inter‐model difference) far exceeds the surface‐forced WMT changes alone, which indicates that the divergent model responses arise from interactions between changes in surface forcing and interior mixing processes. This model divergence demonstrates an important source of uncertainty in climate modeling, and indicates that accurate shelf processes together with scenarios accounting for AIS melting are necessary for robust projections of the deep ocean's response to anthropogenic forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Dependence of Mean Climate State on Shortwave Absorption by Water Vapor

Abstract State-of-the-art climate models exhibit significant spread in the climatological value of atmospheric shortwave absorption (SWA). This study investigates both the possible causes and climatic impacts of this SWA intermodel spread. The intermodel spread of global-mean SWA largely originates from the intermodel difference in water vapor shortwave absorptivity. Hence, we alter the water vapor shortwave absorptivity in the Community Earth System Model, version 1, with the Community Atmosphere Model, version 4 (CESM1-CAM4). Increasing the water vapor shortwave absorptivity leads to a reduction in global-mean precipitation and a La Niña–like cooling over the tropical Pacific. The global-mean atmospheric energy budget suggests that the precipitation is suppressed as a way to compensate for the increased SWA. The precipitation reduction is driven by the weakened surface winds, stabilized planetary boundary layer, and surface cooling. The La Niña–like cooling over the tropical Pacific is attributed to the zonal asymmetry of climatological evaporative damping efficiency and the low cloud enhancement over the eastern basin. Complementary fixed SSTs simulations suggest that the latter is more fundamental and that it primarily arises from atmospheric processes. Consistent with our experiments, the CMIP5/6 models with a higher global-mean SWA tend to produce tropical Pacific toward a more La Niña–like mean state, highlighting the possible role of water vapor shortwave absorptivity for shaping the mean-state climate patterns.

Kim, Hanjun↗
DOE OSTI2020

Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6

Abstract. Human land use activities have resulted in large changes to the biogeochemical and biophysical properties of the Earth's surface, with consequences for climate and other ecosystem services. In the future, land use activities are likely to expand and/or intensify further to meet growing demands for food, fiber, and energy. As part of the World Climate Research Program Coupled Model Intercomparison Project (CMIP6), the international community has developed the next generation of advanced Earth system models (ESMs) to estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon–climate system. A new set of historical data based on the History of the Global Environment database (HYDE), and multiple alternative scenarios of the future (2015–2100) from Integrated Assessment Model (IAM) teams, is required as input for these models. With most ESM simulations for CMIP6 now completed, it is important to document the land use patterns used by those simulations. Here we present results from the Land-Use Harmonization 2 (LUH2) project, which smoothly connects updated historical reconstructions of land use with eight new future projections in the format required for ESMs. The harmonization strategy estimates the fractional land use patterns, underlying land use transitions, key agricultural management information, and resulting secondary lands annually, while minimizing the differences between the end of the historical reconstruction and IAM initial conditions and preserving changes depicted by the IAMs in the future. The new approach builds on a similar effort from CMIP5 and is now provided at higher resolution (0.25∘×0.25∘) over a longer time domain (850–2100, with extensions to 2300) with more detail (including multiple crop and pasture types and associated management practices) using more input datasets (including Landsat remote sensing data) and updated algorithms (wood harvest and shifting cultivation); it is assessed via a new diagnostic package. The new LUH2 products contain > 50 times the information content of the datasets used in CMIP5 and are designed to enable new and improved estimates of the combined effects of land use on the global carbon–climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon Outgassing in the Antarctic Circumpolar Current Is Supported by Ekman Transport From the Sea Ice Zone in an Observation‐Based Seasonal Mixed‐Layer Budget

Despite its importance for the global cycling of carbon, there are still large gaps in our understanding of the processes driving annual and seasonal carbon fluxes in the high-latitude Southern Ocean. This is due in part to a historical paucity of observations in this remote, turbulent, and seasonally ice-covered region. Here, we use autonomous biogeochemical float data spanning 6 full seasonal cycles and with circumpolar coverage of the Southern Ocean, complemented by atmospheric reanalysis, to construct a monthly climatology of the mixed layer budget of dissolved inorganic carbon (DIC). We investigate the processes that determine the annual mean and seasonal cycle of DIC fluxes in two different zones of the Southern Ocean—the Sea Ice Zone (SIZ) and Antarctic Southern Zone (ASZ). We find that, annually, mixing with carbon-rich waters at the base of the mixed layer supplies DIC which is, in the ASZ, either used for net biological production or outgassed to the atmosphere. In contrast, in the SIZ, where carbon outgassing and the biological pump are weaker, the surplus of DIC is instead advected northward to the ASZ. In other words, carbon outgassing in the southern Antarctic Circumpolar Current (ACC), which has been attributed to remineralized carbon from deep water upwelled in the ACC, is also due to the wind-driven transport of DIC from the SIZ. These results stem from the first observation-based carbon budget of the circumpolar Southern Ocean and thus provide a useful benchmark to evaluate climate models, which have significant biases in this region.

Sauvé, Jade↗
DOE OSTI2024

Anthromes and forest carbon responses to global change

Human effects on ecosystems date back thousands of years, and anthropogenic biomes—anthromes—broadly incorporate the effects of human population density and land use on ecosystems. Forests are integral to the global carbon cycle, containing large biomass carbon stocks, yet their responses to land use and climate change are uncertain but critical to informing climate change mitigation strategies, ecosystem management, and Earth system modeling. Using an anthromes perspective and the site locations from the Global Forest Carbon (ForC) Database, we compare intensively used, cultured, and wildland forest lands in tropical and extratropical regions. We summarize recent past (1900-present) patterns of land use intensification, and we use a feedback analysis of Earth system models from the Coupled Model Intercomparison Project Phase 6 to estimate the sensitivity of forest carbon stocks to CO 2 and temperature change for different anthromes among regions. Modeled global forest carbon stock responses are positive for CO 2 increase but neutral to negative for temperature increase. Across anthromes (intensively used, cultured, and wildland forest areas), modeled forest carbon stock responses of temperate and boreal forests are less variable than those of tropical forests. Tropical wildland forest areas appear especially sensitive to CO 2 and temperature change, with the negative temperature response highlighting the potential vulnerability of the globally significant carbon stock in tropical forests. The net effect of anthropogenic activities—including land-use intensification and environmental change and their interactions with natural forest dynamics—will shape future forest carbon stock changes. These interactive effects will likely be strongest in tropical wildlands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

FACETS Dynamical Downscaling Simulations over North America by the CAM-MPAS Variable-Resolution Model

Comprehensive assessment of climate datasets created by statistical or dynamical models is important for effectively communicating model projection and associated uncertainty to stakeholders and decision-makers. The Department of Energy FACETS project aims to foster such communication through development of metrics and their demonstration on a hierarchy of downscaled climate datasets to quantify aspects of climate change projections that are credible, particularly for supporting decisions related to the energy-water-land nexus. As a part of this effort, we have produced a regional climate dataset using the Model for Prediction Across Scales coupled to the Community Atmosphere Model (CAM-MPAS). This global modeling framework is configured with variable-resolution meshes featuring higher resolutions over North America, as well as quasi-uniform resolution meshes across the globe. The variable-resolution configurations allow fine-scale features to be better resolved inside the refinement and interact with global-scale circulations. The dataset includes multiple uniform- (240km and 120km) and variable-resolution (200-50km, 100-25km, and 46-12km) simulations that are designed to be compatible with other regional climate simulations that contribute to the hierarchy of downscaled climate datasets of the project. Furthermore, the dataset consists of simulations for both the present-day (1989-2010) and future (2079-2100) climate and post-processing of the model output has been coordinated across the project for consistency to facilitate common analysis across the hierarchy of datasets. Altogether, this CAM-MPAS model dataset provides a unique opportunity to assess the influence of resolutions and modeling framework on model credibility and climate change projection.

Sakaguchi, Koichi↗
DOE OSTI2023

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing Global and Local Radiative Feedbacks Based on AGCM Simulations for 1980–2014/2017

In order to avoid contamination of unobservable and uncertain effective radiative forcing (ERF) on the diagnosis of radiative feedbacks based on short-term climate variability, we verify the Kernel-Gregory feedback calculation method using atmospheric model experiments with prescribed ERF. We show that both clear-sky radiative fluxes and all-sky radiative feedbacks have a closure between model simulations and kernel derivations. A near-zero global mean net cloud feedback is found in the simulation with prescribed ERF, which results in a more negative global net climate feedback, -2 W m -2 K -1 . Consistent with AMIP6 ensemble mean results, the lapse rate feedback is the largest contributor among all feedbacks to temperature amplification over the three poles (Arctic, Antarctic and Tibetan Plateau), followed by surface albedo feedback and Planck feedback deviation from its global mean. Except for the higher surface albedo feedback in the Antarctic, other feedbacks are almost same between the Arctic and Antarctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Microbial Evolution Drives Adaptation of Substrate Degradation on Decadal to Centennial Time Scales Relevant to Global Change

ABSTRACT Understanding microbial adaptation is crucial for predicting how soil carbon dynamics and global biogeochemical cycles will respond to climate change. This study employs the DEMENT model of microbial decomposition, along with empirical mutation and dispersal rates, to explore the roles of mutation and dispersal in the adaptation of soil microbial populations to shifts in litter chemistry, changes that are anticipated with climate‐driven vegetation dynamics. Following a change in litter chemistry, mutation generally allows for a higher rate of litter decomposition than dispersal, especially when dispersal predominantly introduces genotypes already present in the population. These findings challenge the common idea that mutation rates are too low to affect ecosystem processes on ecological timescales. These results demonstrate that evolutionary processes, such as mutation, can help maintain ecosystem functioning as the climate changes.

Abs, Elsa↗
DOE OSTI2023

Deep Learning Parameterization of Vertical Wind Velocity Variability via Constrained Adversarial Training

Atmospheric models with typical resolution in the tenths of kilometers cannot resolve the dynamics of air parcel ascent, which varies on scales ranging from tens to hundreds of meters. Small-scale wind fluctuations are thus characterized by a subgrid distribution of vertical wind velocity W with standard deviation σ W . The parameterization of σ W is fundamental to the representation of aerosol–cloud interactions, yet it is poorly constrained. Using a novel deep learning technique, this work develops a new parameterization for σ W merging data from global storm-resolving model simulations, high-frequency retrievals of W , and climate reanalysis products. The parameterization reproduces the observed statistics of σ W and leverages learned physical relations from the model simulations to guide extrapolation beyond the observed domain. Incorporating observational data during the training phase was found to be critical for its performance. The parameterization can be applied online within large-scale atmospheric models, or offline using output from weather forecasting and reanalysis products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Potential Applications of Quantum Computing at Los Alamos National Laboratory, v0.3.0

Since the scientific revolution in the 16th and 17th centuries, the process of scientific discovery has followed an iterative feedback process of observation, hypothesis development and testing with physical experiments, which is widely referred to as the scientific method. This process remained largely unchanged until the middle of the 20th century, when the emergence of digital computers empowered scientist to build and inspect detailed simulations of physical phenomena. Over the last century, computational tools have transformed modern approaches to scientific discovery by enabling fast and affordable hypothesis testing before physical experiments are conducted, shown in Figure 1-1. Some notable examples include: global climate forecasts to understand how the environment may change over decades [130]; modeling the behavior of plasma to design fusion reactors [59]; and understanding the behavior of molecules in biological processes [161, 223].

36 MATERIALS SCIENCE↗
DOE OSTI2024

Environmental controls on isolated convection during the Amazonian wet season

The Amazon rainforest is a vital component of the global climate system, influencing the hydrological cycle and tropical circulation. However, understanding and modeling the evolution of convection in this region remain a scientific challenge. Here, we assess the environmental conditions associated with shallow, congestus, and isolated deep convection days during the wet season (December to April), employing measurements from the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment and large-scale wind fields from the constrained variational analysis. Composites of deep days show moister than average conditions below 3 km early in the morning. Analyzing the water budget at the surface through observations only, we estimated the water vapor convergence term as a residual of the water balance closure. Convergence remains nearly zero during the deep days until early afternoon (13:00 LST), when it becomes a dominant factor in the water budget. At 14:00 LST, the deep days experience a robust upward large-scale vertical velocity, especially above 4 km, which supports the shallow-to-deep convective transition occurring around 16:00–17:00 LST. In contrast, shallow and congestus days exhibit drier pre-convective conditions, along with diurnal water vapor divergence and large-scale subsidence that extend from the surface to the lower free troposphere. Moreover, afternoon precipitation exhibits the strongest linear correlation (0.6) with large-scale vertical velocity, nearly double the magnitude observed for other environmental factors, even moisture, at different levels and periods of the day. Precipitation also exhibits a moderate increase with low-level wind shear, while upper-level shear has a relatively minor negative impact on convection.

Environmental Sciences & Ecology↗
DOE OSTI2022

Reconciliation of asynchronous satellite-based $\mathrm{NO_2}$ and $\mathrm{XCO_2}$ enhancements with mesoscale modeling over two urban landscapes

Fossil fuel carbon dioxide (CO 2ff ), the main driver of global warming and climate change, is often co-emitted with nitrogen oxides (NO x ) and precursors to ground-level ozone from anthropogenic sources like power plants or vehicles. In urban and suburban areas, satellite-based NO 2 can be used as a proxy to track the emissions of CO 2ff . Because of NO 2 ’s shorter lifetime, urban NO 2 plumes are more distinguishable from backgrounds and more sensitive to variations in emissions. However, the combination of these two gases is limited by the asynchrony among NO 2 and CO 2 monitoring satellites. We used CO 2ff simulated by the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model to reconcile the tropospheric NO 2 vertical column density (VCD) from the Tropospheric Monitoring Instrument (TROPOMI) and column-averaged dry-air mole fractions of carbon dioxide enhancements (ΔXCO 2 ) from Orbiting Carbon Observatory 3 (OCO-3) Snapshot Area Maps (SAMs) over a multicity area, Washington D.C.-Baltimore (DC-Balt), and a basin city, Mexico City. NO 2 /CO 2ff ratios over DC-Balt are smaller than Mexico City, indicative of stricter emission restrictions, a more combustion-efficient vehicle fleet, and higher combustion efficiency due to lower altitude in DC-Balt. For single-track cases, the spatial correlations between NO 2 and ΔXCO 2 over Mexico City are stronger than DC-Balt because the NO 2 and CO 2 are mostly trapped in the valley of Mexico City, while DC-Balt is severely affected by distant sources (i.e., US East Coast cities). Using multi-track averaging, spatial correlation coefficients increase with the number of days used for averaging. The correlations reached a maximum when averaging >12 continuous images for DC-Balt and > 10 continuous images for Mexico City. This finding indicates that multi-track averaging using modeled CO 2ff as a proxy is helpful to filter the noise in single-track images, to cancel the interference from distant sources, and to magnify correlations between NO 2 and CO 2ff . Mexico City showed stronger spatial correlations but weaker temporal correlations than DC-Balt due to biomass burning hot spots and large transport errors caused by the trapping effects of the surrounding mountains. Tracking the 20-day moving average of CO 2ff emissions using TROPOMI NO 2 seems technically feasible, considering the relationship between correlation coefficients and the number of available satellite images.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Fire weather compromises forestation-reliant climate mitigation pathways

Forestation can contribute to climate change mitigation. However, increasing frequency and intensity of climate extremes are posed to have profound impact on forests and consequently on the mitigation potential of forestation efforts. In this perspective, we critically assess forestation-reliant climate mitigation scenarios from five different integrated assessment models (IAMs) by showcasing the spatially explicit exposure of forests to fire weather and the simulated increase in global annual burned area. We provide a detailed description of the feedback from climate change to forest carbon uptake in IAMs. Few IAMs are currently accounting for feedback mechanisms like loss from fire disturbance. Consequently, many forestation areas proposed by IAM scenarios will be exposed to fire-promoting weather conditions and without costly prevention measures might be object to frequent burning. We conclude that the actual climate mitigation portfolio in IAM scenarios is subject to substantial uncertainty and that the risk of overly optimistic estimates of negative emission potential of forestation should be avoided. As a way forward we propose how to integrate more detailed climate information when modeling climate mitigation pathways heavily relying on forestation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mean Climate and Tropical Rainfall Variability in Aquaplanet Simulations Using the Model for Prediction Across Scales‐Atmosphere

Abstract Aquaplanet experiments are important tools for understanding and improving physical processes simulated by global models; yet, previous aquaplanet experiments largely differ in their representation of subseasonal tropical rainfall variability. This study presents results from aquaplanet experiments produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A)—a community model specifically designed to study weather and climate in a common framework. The mean climate and tropical rainfall variability simulated by MPAS‐A with varying horizontal resolution were compared against results from a recent suite of aquaplanet experiments. This comparison shows that, regardless of horizontal resolution, MPAS‐A produces the expected mean climate of an aquaplanet framework with zonally symmetric but meridionally varying sea‐surface temperature. MPAS‐A, however, has a stronger signal of tropical rainfall variability driven by convectively coupled equatorial waves. Sensitivity experiments with different cumulus parameterizations, physics packages, and vertical grids consistently show the presence of those waves, especially equatorial Kelvin waves, in phase with lower‐tropospheric convergence. Other models do not capture such rainfall‐kinematics phasing. These results suggest that simulated tropical rainfall variability depends not only on the cumulus parameterization (as suggested by previous studies) but also on the coupling between physics and dynamics of climate and weather prediction models.

Rios‐Berrios, R.↗
DOE OSTI2023

Understanding local plant extinctions before it is too late: bridging evolutionary genomics with global ecology

Understanding evolutionary genomic and population processes within a species range is key to anticipating the extinction of plant species before it is too late. However, most models of biodiversity risk under global change do not account for the genetic variation and local adaptation of different populations. Population diversity is critical to understanding extinction because different populations may be more or less susceptible to global change and, if lost, would reduce the total diversity within a species. Here, two new modeling frameworks advance our understanding of extinction from a population and evolutionary angle: Rapid climate change-driven disruptions in population adaptation are predicted from associations between genomes and local climates. Furthermore, losses of population diversity from global land-use transformations are estimated by scaling relationships of species' genomic diversity with habitat area. Overall, these global eco-evolutionary methods advance the predictability – and possibly the preventability – of the ongoing extinction of plant species.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Modeling the Economic and Environmental Impacts of Land Scarcity Under Deep Uncertainty

Abstract Land scarcity is increasing over time, driven by complex multisector dynamics. The impacts of land scarcity on the economy and environment are multi‐faceted and regional, so any action to convert land will contain inherent tradeoffs. These impacts are complicated by the deeply uncertain evolution of the various sectors influencing land scarcity. A need therefore exists to provide multi‐metric and multi‐sector assessments that are robust to myriad uncertainties. Land conservation effectively limits the supply of productive land, while biofuel consumption increases the demand and competition for that land, and how these dynamics individually and jointly propagate to economic and environmental impacts is an important open question. To address this, we adopt the Global Change Analysis Model (GCAM) that has representations of various important systems including the climate, macroeconomic, energy, agriculture and land, and water resources systems. Various scenarios of increased land demand (from biofuels) and decreased land supply (from conservation) under various socioeconomic scenarios drawn from the SSPs were simulated using GCAM. We find that while biofuel consumption and land conservation reduce carbon emissions, this comes at the cost of higher food prices, reduced crop production, and increased water withdrawals. Additionally, some regions experience these tradeoffs more severely than others and are more heavily impacted from the same biofuel mandate or by an additional percent of protected land. These and other findings highlight the importance of multisector modeling frameworks that capture many cross‐sector linkages, and acknowledge the important uncertainties confronting the human‐Earth system when making any analysis of land scarcity impacts.

54 ENVIRONMENTAL SCIENCES↗