Abiotrophia defectiva is a pathogen of the oral, gastrointestinal, and urinary tracts that can cause significant systemic disease with uniquely negative blood cultures depending on the growth medium. Prior cases note possible seeding from relatively common procedures such as routine dental work and prostate biopsies, however case literature describes prior infectious complications to include infective endocarditis, brain abscess formation, and spondylodiscitis. While prior cases describe some aspects of these presentations, we highlight a case of a 64-year-old male who presented to the emergency department (ED) f5or acute onset of low back pain with fever symptoms four days after an outpatient transrectal ultrasound-guided needle biopsy of the prostate, with a prior dental extraction described four weeks prior to arrival. Findings on initial ED presentation and subsequent hospitalization revealed infective spondylodiscitis, endocarditis, and brain abscess formation. This is the only cases noted in literature with all three infection locations with dual risk factors of dental and prostate procedures prior to symptom onset. This case highlights the multifocal illness that can complicate Abiotrophia defectiva infections, and the importance of thorough ED evaluation and multiservice approach for consultation and treatment.

Typically in human-earth system modeling studies, feedbacks between the earth and human systems are analyzed by passing information between independent models. The reliance on existing Earth System Model outputs limits the ability to explore feedbacks under arbitrary scenarios and equally important limits the ability to explore large-scale uncertainty in these interactions. In this study we explore a wide range of climate uncertainties and incorporate the implications of increased cooling hydrofluorocarbons emissions. We implement a statistical relationship between global mean temperature change and heating and cooling degree days that allows us to produce changes in building energy demands within GCAM at every time step and every region. While there is a general agreement in the literature that increasing temperatures will increase cooling energy demands and decrease heating energy demands, there has been no fully-coupled analysis of this dynamic that would, for example, account for the feedbacks on hydrofluorocarbons from increased cooling demands. The variation in the spatial distribution of temperatures leads to substantial variation in the change in cooling and heating energy across regions, with regions like USA, India and Sub-Saharan Africa experiencing a factor of two difference in cooling demands. While the feedbacks between building energy demand and global mean temperature are modest by themselves, this study prompts future research on coupled human-earth system feedbacks, in particular in regards to land, water, and other energy infrastructure.

Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

We use simple pattern scaling and time-shift to emulate changes in a set of climate extreme indices under future scenarios, and we evaluate the emulators’ accuracy. We propose an error metric that separates systematic emulation errors from discrepancies between emulated and target values due to internal variability, taking advantage of the availability of climate model simulations in the form of initial condition ensembles. We compute the error metric at grid-point scale, and we show geographically resolved results, or aggregate them as global averages. We use a range of scenarios spanning global temperature increases by the end of the century of 1.5 C and 2.0 C compared to a pre-industrial baseline, and two higher trajectories, RCP4.5 and RCP8.5. With this suite of scenarios we can test the effects on the error of the size of the temperature gap between emulation origin and target scenarios. We find that in the emulation of most indices the dominant source of discrepancy is internal variability. For at least one index, however, counting exceedances of a high temperature threshold, significant portions of the globally aggregated discrepancy and its regional pattern originate from the systematic emulation error. The metric also highlights a fundamental difference in the two methods related to the simulation of internal variability, which is significantly resized by simple pattern scaling. This aspect needs to be considered when using these methods in applications where preserving variability for uncertainty quantification is important. We propose our metric as a diagnostic tool, facilitating the formulation of scientific hypotheses on the reasons for the error. In the meantime, we show that for many impact relevant indices these two well established emulation techniques perform accurately when measured against internal variability, establishing the fundamental condition for using them to represent climate drivers in impact modeling.

Future changes in climate and socioeconomic systems will drive both the availability and use of water resources, leading to evolutions in scarcity. The contributions of both systems can be quantified individually to understand the impacts around the world, but also combined to explore how the coevolution of energy-water-land systems affects not only the driver behind water scarcity changes, but how human and climate systems interact in tandem to alter water scarcity. Here we investigate the relative contributions of climate and socioeconomic systems on water scarcity under the Shared Socioeconomic Pathways-Representative Concentration Pathways framework. While human systems dominate changes in water scarcity independent of socioeconomic or climate future, the sign of these changes depend particularly on the socioeconomic scenario. Under specific socioeconomic futures, human-driven water scarcity reductions occur in up to 44% of the global land area by the end of the century.

Drought research customarily uses statistics collected over a reference period to establish a threshold for declaring a region to be in a drought, or to estimate baseline return periods. Often these statistics involve quantile values from the tails of the distribution of reference period observations, such as 10th or even 1st percentile values. The length of the reference period is dictated by the available record length; often it is no longer than 50-100 years. Depending on the purpose for which the drought study is intended, the unit of time used as the averaging period for the hydrologic or meteorologic variables of interest is often as small as one month. In this circumstance, percentile values are each based on at most 100 data points. We show here that the statistical uncertainty resulting from these small sample sizes for estimating the threshold value is sufficient to compromise many types of analysis. We provide formulae for calculating the statistical uncertainties caused by limited record lengths and for estimating the record length needed to achieve a specified level of accuracy in an analysis. Our results show that datasets of 100 years or less are approximately 1/10 the length needed to achieve the level of reliability required for many applications. We also summarize options for augmenting the historical record when the existing record length is not long enough to support analysis at the desired level of accuracy.

We investigate techniques for using deep neural networks to produce surrogate models for short-term climate forecasts. A convolutional neural network is trained on 97 years of monthly precipitation output from the 1pctCO2 run (the CO2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast and does not show substantially degraded performance even when the forecast length is extended to 120 months. The model is prone to underpredicting precipitation in areas characterized by intense precipitation events. Scheduled sampling (forcing the model to gradually use its own past predictions rather than ground truth) is essential for avoiding amplification of early forecasting errors. However, the use of scheduled sampling also necessitates preforecasting (generating forecasts prior to the first forecast date) to obtain adequate performance for the first few prediction time steps. We document the training procedures and hyperparameter optimization process for researchers who wish to extend the use of neural networks in developing surrogate models.

Earth system models (ESMs), which simulate the physics and chemistry of the global atmosphere, land, and ocean, are often used to generate future projections of climate change scenarios. These models are far too computationally intensive to run repeatedly, but limited sets of runs are insufficient for some important applications, like adequately sampling distribution tails to characterize extreme events. As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM. Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator. In doing so, we gain the ability to produce daily weather data that is consistent with what ESM might output over any chosen scenario. In particular, the GAN is aimed at representing a joint probability distribution over space, time, and climate variables, enabling the study of correlated extreme events, such as floods, droughts, or heatwaves.

Simple climate models (SCMs) are computationally efficient and capable of emulating global mean output of more complex Earth system models (ESMs). In doing so, SCMs can play a critical role in climate research as stand‐ins for the computationally more expensive models, especially in studies involving low, spatial, and/or temporal resolution, providing more computationally efficient sources of climate data. Here we use Hector v2.5.0 to emulate the multiforcing historical and RCP scenario output for 31 concentration and seven emission‐driven ESMs. When calibrating Hector, sufficient calibration data must be used to constrain the model; otherwise, climate and/or carbon parameters affecting physical processes may be able to trade off with one another, allowing for solutions to use physically unreasonable fitted parameter values as well as limiting the application of the SCM as an emulator. We also present a novel methodology that uses the ESM range as a calibration data, which can be adopted when faced with missing variable output from a specific model.

Earth System Models (ESMs) are excellent tools for quantifying many aspects of future climate dynamics but are too computationally expensive to produce large collections of scenarios for downstream users of ESM data. In particular, many researchers focused on the impacts of climate change require large collections of ESM runs to rigorously study the impacts to both human and natural systems of low-frequency high-importance events, such as multi-year droughts. Climate model emulators provide an effective mechanism for filling this gap, reproducing many aspects of ESMs rapidly but with lower precision. The fldgen v1.0 R package quickly generates thousands of realizations of gridded temperature fields by randomizing the residuals of pattern scaling temperature output from any single ESM, retaining the spatial and temporal variance and covariance structures of the input data at a low computational cost. The fldgen v2.0 R package described here extends this capability to produce joint realizations of multiple variables, with a focus on temperature and precipitation in an open source software package available for community use (https://github.com/jgcri/fldgen). This substantially improves the fldgen package by removing the requirement that the ESM variables be normally distributed, and will enable researchers to quickly generate covarying temperature and precipitation data that are synthetic but faithful to the characteristics of the original ESM.

This paper describes GCAM v5.1, an open source model that represents the linkages between energy, water, land, climate, and economic systems. GCAM is a market equilibrium model, is global in scope, and operates from 1990 to 2100 in 5-year time steps. It can be used to examine, for example, how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals, or how changes in one region's demand for energy affect energy, water, and land in other regions. This paper describes the model, including its assumptions, inputs, and outputs. We then use 11 scenarios, varying the socioeconomic and climate policy assumptions, to illustrate the results from the model. The resulting scenarios demonstrate a wide range of potential future energy, water, and land uses. We compare the results from GCAM v5.1 to historical data and to future scenario simulations from earlier versions of GCAM and from other models. Finally, we provide information on how to obtain the model.

Earth system models (ESMs) are the gold standard for producing future projections of climate change, but running them is difficult and costly, and thus researchers are generally limited to a small selection of scenarios. This paper presents a technique for detailed emulation of the Earth system model (ESM) temperature output, based on the construction of a deterministic model for the mean response to global temperature. The residuals between the mean response and the ESM output temperature fields are used to construct variability fields that are added to the mean response to produce the final product. The method produces grid-level output with spatially and temporally coherent variability. Output fields include random components, so the system may be run as many times as necessary to produce large ensembles of fields for applications that require them. We describe the method, show example outputs, and present statistical verification that it reproduces the ESM properties it is intended to capture. This method, available as an open-source R package, should be useful in the study of climate variability and its contribution to uncertainties in the interactions between human and Earth systems.

:::::::::::: Internal ::::::::::::::::: Variability :::::: and :::::::::::::::::: Space-Time ::::::::::::::::::: Correlation for Earth System Models Robert Link1, Abigail Snyder1, Cary Lynch2, Corinne Hartin1, Ben Kravitz3,4, and Ben Bond-Lamberty1 1Pacific Northwest National Laboratory, Joint Global Change Research Institute, 5825 University Research Ct., College Park, MD, USA 2Connecticut Department of Energy and Environmental Protection, 10 Franklin Square New Britain, CT, USA 3Department of Earth and Atmospheric Sciences, Indiana University, 1001 E. 10th St., Bloomington, IN, USA 4Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA, USA Correspondence: Robert Link (robert.link@pnnl.gov)

With the ability to simulate historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degree, the Python package Xanthos version 1 provided a solid foundation for continuing advancements in global water dynamics science. The goal of Xanthos version 2 was to build upon previous investments by creating a Python framework where core components of the model (potential evapotranspiration (PET), runoff generation, and river routing) could be interchanged or extended without having to start from scratch. Xanthos 2 utilizes a component-style architecture which enables researchers to quickly incorporate and test cutting-edge research in a stable modeling environment prebuilt with diagnostics. Major advancements for Xanthos 2 were also achieved by the creation of a robust default configuration with a calibration module, hydropower modules, and new PET modules, which are now available to the scientific community. Funding statement: This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone.

The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility. Funding statement: Primary support for this work was provided by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Additional support was provided by the U.S. Department of Energy Offices of Fossil Energy, Nuclear Energy, and Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency.

'gcamland v1.0' is an open source R package that was built to allocate land across a variety of uses based on changes in agricultural yield and commodity price. The land allocation algorithm is based on the one included in the Global Change Assessment Model (GCAM). 'gcamland v1.0' includes the ability to run in a historical mode, enabling model validation and parameter estimation, or in a future mode, simulating changes in land use/land cover in the future. For both modes, 'gcamland v1.0' can run a single simulation or a large ensemble of simulations with different parameters. When ensembles are generated in the historical mode, 'gcamland v1.0' calculates the likelihood of a given parameter set by comparing to observational data. 'gcamland v1.0' is publicly available via GitHub and has can be adjusted to represent alternative scenarios or configured to different regions and land types. Funding statement: This research was supported by the Office of Science of the U.S. Department of Energy as part of the Multi-Sector Dynamics Program. Pacific Northwest National Laboratory is operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830.