Full-complexity Model Research Articles

Exploring Subtropical Stratocumulus Multiple Equilibria Using a Mixed-Layer Model

Abstract Stratocumulus clouds cover about a fifth of Earth’s surface, and due to their albedo and low-latitude location, they have a strong effect on Earth’s radiation budget. Previous studies using large-eddy simulations have shown that multiple equilibria (both stratocumulus-covered and cloud-free/scattered cumulus states) exist as a function of fixed SST, with relevance to equatorward advected air masses. Multiple equilibria have also been found as a function of atmospheric CO2, with a subtropical SST nearly 10 K higher in the cloud-free state and with suggested relevance to warm climate dynamics. In this study, we use a mixed-layer model with an added surface energy balance and the ability to simulate both the stratocumulus (coupled) and cloud-free/scattered cumulus (decoupled) states using a “stacked” mixed-layer approach to study both types of multiple equilibria and the corresponding hysteresis. The model’s simplicity and computational efficiency allow us to qualitatively explore the mechanisms critical to the stratocumulus cloud instability and hysteresis as well as isolate key processes that allow for multiple equilibria via mechanism-denial experiments not possible with a full-complexity model. For the hysteresis in fixed SST, we find that decoupling can occur due to either enhanced entrainment warming or a reduction in cloud-top longwave cooling. The critical SST at which decoupling occurs is highly sensitive to precipitation and entrainment parameterizations. In the CO2 hysteresis, decoupling occurs in the simple model used even without the inclusion of SST–cloud cover feedbacks, and the width of the hysteresis displays the same sensitivities as the fixed SST case. Overall, the simple model analysis and results motivate further studies using higher complexity models.

A mathematical model of iron import and trafficking in wild-type and Mrs3/4\u0394\u0394 yeast cells

BackgroundIron plays crucial roles in the metabolism of eukaryotic cells. Much iron is trafficked into mitochondria where it is used for iron-sulfur cluster assembly and heme biosynthesis. A yeast strain in which Mrs3/4, the high-affinity iron importers on the mitochondrial inner membrane, are deleted exhibits a slow-growth phenotype when grown under iron-deficient conditions. However, these cells grow at WT rates under iron-sufficient conditions. The object of this study was to develop a mathematical model that could explain this recovery on the molecular level.ResultsA multi-tiered strategy was used to solve an ordinary-differential-equations-based mathematical model of iron import, trafficking, and regulation in growing Saccharomyces cerevisiae cells. At the simplest level of modeling, all iron in the cell was presumed to be a single species and the cell was considered to be a single homogeneous volume. Optimized parameters associated with the rate of iron import and the rate of dilution due to cell growth were determined. At the next level of complexity, the cell was divided into three regions, including cytosol, mitochondria, and vacuoles, each of which was presumed to contain a single form of iron. Optimized parameters associated with import into these regions were determined. At the final level of complexity, nine components were assumed within the same three cellular regions. Parameters obtained at simpler levels of complexity were used to help solve the more complex versions of the model; this was advantageous because the data used for solving the simpler model variants were more reliable and complete relative to those required for the more complex variants. The optimized full-complexity model simulated the observed phenotype of WT and Mrs3/4ΔΔ cells with acceptable fidelity, and the model exhibited some predictive power.ConclusionsThe developed model highlights the importance of an FeII mitochondrial pool and the necessary exclusion of O2 in the mitochondrial matrix for eukaryotic iron-sulfur cluster metabolism. Similar multi-tiered strategies could be used for any micronutrient in which concentrations and metabolic forms have been determined in different organelles within a growing eukaryotic cell.

A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings

Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based on simplified linear models, which support faster computation but also present some limitations regarding interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured according to the expected building conditions (weather and occupancy). The system has been implemented and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding energy savings around 35% during the intermediate winter season and 20% in the whole winter season with respect to the current operation of the heating equipment.

Sensitivity of the Eocene climate to CO<sub>2</sub> and orbital variability

Abstract. The early Eocene, from about 56 Ma, with high atmospheric CO2 levels, offers an analogue for the response of the Earth's climate system to anthropogenic fossil fuel burning. In this study, we present an ensemble of 50 Earth system model runs with an early Eocene palaeogeography and variation in the forcing values of atmospheric CO2 and the Earth's orbital parameters. Relationships between simple summary metrics of model outputs and the forcing parameters are identified by linear modelling, providing estimates of the relative magnitudes of the effects of atmospheric CO2 and each of the orbital parameters on important climatic features, including tropical–polar temperature difference, ocean–land temperature contrast, Asian, African and South (S.) American monsoon rains, and climate sensitivity. Our results indicate that although CO2 exerts a dominant control on most of the climatic features examined in this study, the orbital parameters also strongly influence important components of the ocean–atmosphere system in a greenhouse Earth. In our ensemble, atmospheric CO2 spans the range 280–3000 ppm, and this variation accounts for over 90 % of the effects on mean air temperature, southern winter high-latitude ocean–land temperature contrast and northern winter tropical–polar temperature difference. However, the variation of precession accounts for over 80 % of the influence of the forcing parameters on the Asian and African monsoon rainfall, and obliquity variation accounts for over 65 % of the effects on winter ocean–land temperature contrast in high northern latitudes and northern summer tropical–polar temperature difference. Our results indicate a bimodal climate sensitivity, with values of 4.36 and 2.54 ∘C, dependent on low or high states of atmospheric CO2 concentration, respectively, with a threshold at approximately 1000 ppm in this model, and due to a saturated vegetation–albedo feedback. Our method gives a quantitative ranking of the influence of each of the forcing parameters on key climatic model outputs, with additional spatial information from singular value decomposition providing insights into likely physical mechanisms. The results demonstrate the importance of orbital variation as an agent of change in climates of the past, and we demonstrate that emulators derived from our modelling output can be used as rapid and efficient surrogates of the full complexity model to provide estimates of climate conditions from any set of forcing parameters.

MACHINE-LEARNING MODELS FOR PREDICTING PATIENT SURVIVAL AFTER LIVER TRANSPLANTATION

In our work we have built models predicting whether a patient will lose an organ after liver transplantation within a specified time horizon. We have used the observations of bilirubin and creatinine in the whole first year after the transplantation to derive predictors capturing not only their static value but also variability. Our models indeed have predictive power which proves the value of incorporating variability of biochemical measurements and it is the first contribution of our paper. The second one is the selection of the best model for the defined problem. We have identified that full-complexity models, such as random forests and gradient boosting, despite having the best predictive power, lack sufficient interpretability which is important in medicine. We have found that generalized additive models (GAM) provide desired interpretability and their predictive power is closer to the predictions of full-complexity models than to the predictions of simple linear models.

Simulating the Black Saturday 2009 smoke plume with an interactive composition-climate model: sensitivity to emissions amount, timing and injection height.

We simulated the high-altitude smoke plume from the early February 2009 Black Saturday bushfires in southeastern Australia using the NASA GISS ModelE2. To the best of our knowledge, this is the first single-plume analysis of biomass burning emissions injected directly into the upper-troposphere/lower stratosphere (UTLS) using a full-complexity composition-climate model. We compared simulated carbon monoxide (CO) to a new Aura TES/MLS joint CO retrieval, focusing on the plume's initial transport eastward, anticyclonic circulation to the north of New Zealand, westward transport in the lower stratospheric easterlies, and arrival over Africa at the end of February. Our goal was to determine the sensitivity of the simulated plume to prescribed injection height, emissions amount and emissions timing from different sources for a full complexity model when compared to Aura. The most realistic plumes were obtained using injection heights in the UTLS, including one drawn from ground-based radar data. A six-hour emissions pulse or emissions tied to independent estimates of hourly fire behavior produced a more realistic plume in the lower stratosphere compared to the same emissions amount being released evenly over 12 or 24-hours. Simulated CO in the plume was highly sensitive to the differences between emissions amounts estimated from the Global Fire Emissions Database and from detailed, ground-based estimates of fire growth. The emissions amount determined not only the CO concentration of the plume, but the proportion of the plume that entered the stratosphere. We speculate that this is due to either or both non-linear CO loss with a weakened OH sink, or plume self-lofting driven by shortwave absorption of the co-emitted aerosols.