1,229 research outputs found
Modeling Perennial Bioenergy Crops in the E3SM Land Model (ELMv2)
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. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high 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 five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems
Recommended from our members
ABC for Climate: Dealing with Expensive Simulators
A single molecule or molecule complex detection method is disclosed in certain aspects, comprising nano- or micro-fluidic channels.U
A review of surrogate models and their application to groundwater modeling
The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a simpler, and hence faster, model which emulates the specified output of a more complex model in function of its inputs and parameters. In this review paper, we summarize surrogate modeling techniques in three categories: data-driven, projection, and hierarchical-based approaches. Data-driven surrogates approximate a groundwater model through an empirical model that captures the input-output mapping of the original model. Projection-based models reduce the dimensionality of the parameter space by projecting the governing equations onto a basis of orthonormal vectors. In hierarchical or multifidelity methods the surrogate is created by simplifying the representation of the physical system, such as by ignoring certain processes, or reducing the numerical resolution. In discussing the application to groundwater modeling of these methods, we note several imbalances in the existing literature: a large body of work on data-driven approaches seemingly ignores major drawbacks to the methods; only a fraction of the literature focuses on creating surrogates to reproduce outputs of fully distributed groundwater models, despite these being ubiquitous in practice; and a number of the more advanced surrogate modeling methods are yet to be fully applied in a groundwater modeling context
Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Childrenâs Environmental Health and Disease Prevention Research
The National Childrenâs Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Childrenâs Environmental Health and Disease Prevention Research that may enhance the National Childrenâs Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatialâtemporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) âUsualâ temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Childrenâs Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period
Autocalibration of the E3SM version 2 atmosphere model using a PCA-based surrogate for spatial fields
Global Climate Model (GCM) tuning (calibration) is a tedious and
time-consuming process, with high-dimensional input and output fields. Experts
typically tune by iteratively running climate simulations with hand-picked
values of tuning parameters. Many, in both the statistical and climate
literature, have proposed alternative calibration methods, but most are
impractical or difficult to implement. We present a practical, robust and
rigorous calibration approach on the atmosphere-only model of the Department of
Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can
be summarized into two main parts: (1) the training of a surrogate that
predicts E3SM output in a fraction of the time compared to running E3SM, and
(2) gradient-based parameter optimization. To train the surrogate, we generate
a set of designed ensemble runs that span our input parameter space and use
polynomial chaos expansions on a reduced output space to fit the E3SM output.
We use this surrogate in an optimization scheme to identify values of the input
parameters for which our model best matches gridded spatial fields of climate
observations. To validate our choice of parameters, we run E3SMv2 with the
optimal parameter values and compare prediction results to expertly-tuned
simulations across 45 different output fields. This flexible, robust, and
automated approach is straightforward to implement, and we demonstrate that the
resulting model output matches present day climate observations as well or
better than the corresponding output from expert tuned parameter values, while
considering high-dimensional output and operating in a fraction of the time
- âŚ