A Shallow‐Deep Unified Stochastic Mass Flux Cumulus Parameterization in the Single Column Community Climate Model
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Soil organic carbon (C) plays a fundamental role in tropical and subtropical soil fertility, agronomic productivity, and soil health. As a tool for understand ecosystems dynamics, mathematical models such as Century have been used to assess soil's capacity to store C in different environments. However, as Century was initially developed for temperate ecosystems, several authors have hypothesized that C storage may be underestimated by Century in Oxisols. We tested the hypothesis that Century model can be parameterized for tropical soils and used to reliably estimate soil organic carbon (SOC) storage. The aim of this study was to investigate SOC storage under two soil types and three textural classes and quantify the sources and magnitude of uncertainty using the Century model. The simulation for SOC storage was efficient and the mean residue was 10 Mg C ha -1 (13%) for n = 91. However, a different simulation bias was observed for soil with <600 g kg -1 of clay was 16.3 Mg C ha -1 (18%) for n = 30, and at >600 g kg -1 of clay, was 4 Mg C ha -1 (5%) for n = 50, respectively. The results suggest a non-linear effect of clay and silt contents on C storage in Oxisols. All types of soil contain nearly 70% of Fe and Al oxides in the clay fraction and a regression analysis showed an increase in model bias with increase in oxides content. Consequently, inclusion of mineralogical control of SOC stabilization by Fe and Al (hydro) oxides may improve results of Century model simulations in soils with high oxides contents
A three-dimensional intermolecular potential energy surface of the Ar–CO complex has been determined by fitting most of the previously reported spectroscopic data, where observed transition frequencies by microwave, millimeter-wave, submillimeter-wave, and infrared spectroscopy were reproduced simultaneously within their experimental accuracies. A free rotor model Hamiltonian considering all the freedom of motions for an atom-diatom system was applied to calculate vibration-rotation energies. A three-dimensional potential energy surface obtained by ab initio calculations at the CCSD(T)-F12b/aug-cc-pV5Z level of theory was parameterized by a model function consisting of 46 parameters. They were used as initial values for the least-squares analysis of the experimental data. A total of 20 parameters were optimized to reproduce all the spectroscopic data.
For particulate-based amendments to be viable for field-scale remediation at the Hanford Site (e.g., the 200 DV-1 Operable Unit), amendment particles need to be delivered a reasonable distance away from an injection well to provide cost effective in situ treatment. Field-scale particle transport models can estimate spatial deposition of amendment particles in the subsurface, which is critical for developing an overall remediation strategy. However, field-scale particle simulations are currently limited due to insufficient simulation capability and a lack of experimental data to validate and parameterize particle transport models. During this fiscal year, the following progress has been made toward a field-scale particle transport modeling evaluation: (1) in addition to the two particle transport models implemented last FY, four additional particle transport models have been implemented within PFLOTRAN; (2) a Python-based pre-screening tool was finalized, enabling users to quickly estimate the particle radius of influence (ROI) for any given particle-amendment system; and (3) an initial compatibility assessment was completed using both particle transport simulations deployed through the pre-screening tool, in conjunction with general guidelines to (a) identify the most suitable amendment particle sizes for various Hanford sediments and (b) evaluate amendment-delivery fluid compatibility. The preliminary compatibility assessment revealed that for amendment delivery success to the various Hanford target formations, amendment particle sizes will likely need to be smaller than the amendment sizes tested in the DV-1 treatability study. It is recommended that amendment particles be decreased in size, or alternative smaller size amendments be obtained from the manufacture, prior to any further experimental testing. Also, preliminary testing suggests that xanthan gum may be the most broadly compatible delivery fluid. Planned laboratory experiments will be instrumental in validating and refining the PFLOTRAN particulate transport model formulations, ultimately enabling predictive capabilities to facilitate the design of field-scale amendment delivery systems. This work consists of acquiring new theoretical or experimental knowledge. The information associated with this report should not be used as design input or operating parameters without additional qualification.
In the coming years, researchers will have at their disposal a host of new observations from advanced space-based sensors (e.g. the Tropical Rainfall Measuring Mission, EOS Terra and PM missions) providing, among other things, a more complete and accurate description of various components of the Earth's hydrological cycle. Also, increasingly more sophisticated and comprehensive geophysical models will provide researchers better tools for simulating the hydrological cycle, and for carrying out mechanistic studies of the role of moist processes in the climate system. In addition, new data sets generated with global four-dimensional data assimilation (4DDA) systems will provide comprehensive and complete information on both the state and forcing of the climate system. Ideally, the 4DDA systems optimally incorporate all relevant information from the observations together with a first guess from a state-of-the-art geophysical model to produce a "best" estimate of the climate state. Furthermore, to the extent that the assimilating models are realistic and are constrained by the observations, they should provide reliable estimates of the associated physical processes or climate forcing fields. While operational weather centers now have a considerable history of providing reliable estimates of the basic atmospheric state variables, the associated processes or diagnostic fields (which are less well constrained by the observations and sensitive to errors in the model's physical parameterizations) are still considered experimental and of uncertain quality. In this study we will examine the current generation of reanalysis products to assess the capabilities of 4DDA systems to represent components of the hydrological cycle. The focus is on the role of the model in providing consistent estimates of moist processes. We will also assess whether current observations provide sufficient constraints on these model- generated fields.
It is often desirable to accurately and efficiently model the behavior of large molecular systems in the condensed phase (thousands to tens of thousands of atoms) over long time scales (from nanoseconds to milliseconds). In these cases, ab initio methods are difficult due to the increasing computational cost with the number of electrons. A more computationally attractive alternative is to perform the simulations at the atomic level using a parameterized function to model the electronic energy. Many empirical force fields have been developed for this purpose. However, the functions that are used to model interatomic and intermolecular interactions contain many fitted parameters obtained from selected model systems, and such classical force fields cannot properly simulate important electronic effects. Furthermore, while such force fields are computationally affordable, they are not reliable when applied to systems that differ significantly from those used in their parameterization. They also cannot provide the information necessary to analyze the interactions that occur in the system, making the systematic improvement of the functional forms that are used difficult. Ab initio force field methods aim to combine the merits of both types of methods. The ideal ab initio force fields are built on first principles and require no fitted parameters. Ab initio force field methods surveyed in this perspective are based on fragmentation approaches and intermolecular perturbation theory. This perspective summarizes their theoretical foundation, key components in their formulation, and discusses key aspects of these methods such as accuracy and formal computational cost. The ab initio force fields considered here were developed for different targets, and this perspective also aims to provide a balanced presentation of their strengths and shortcomings. Finally, this perspective suggests some future directions for this actively developing area.
The onset of the southeast Asian monsoon during 1997 and 1998 was simulated by coupling a mesoscale atmospheric model (MM5) and a detailed, land surface model, PLACE (the Parameterization for Land-Atmosphere-Cloud Exchange). The rainfall results from the simulations were compared with observed satellite data from the TRMM (Tropical Rainfall Measuring Mission) TMI (TRMM Microwave Imager) and GPCP (Global Precipitation Climatology Project). The control simulation with the PLACE land surface model and variable sea surface temperature captured the basic signatures of the monsoon onset processes and associated rainfall statistics. Sensitivity tests indicated that simulations were sigmficantly improved by including the PLACE land surface model. The mechanism by which the land surface processes affect the moisture transport and the convection during the onset of the southeast Asian monsoon were analyzed. The results indicated that land surface processes played an important role in modifying the low-level wind field over two major branches of the circulation: the southwest low-level flow over the Indo-china peninsula and the northern, cold frontal intrusion from southern China. The surface sensible and latent heat fluxes modified the low-level temperature distribution and gradient, and therefore the low-level wind due to the thermal wind effect. The more realistic forcing of the sensible and latent heat fluxes from the detailed, land surface model improved the low-level wind simulation apd associated moisture transport and convection.
The onset of the southeast Asian monsoon during 1997 and 1998 was simulated by coupling a mesoscale atmospheric model (MM5) and a detailed, land surface model, PLACE (the Parameterization for Land-Atmosphere-Cloud Exchange). The rainfall results from the simulations were compared with observed satellite data from the TRMM (Tropical Rainfall Measuring Mission) TMI (TRMM Microwave Imager) and GPCP (Global Precipitation Climatology Project). The control simulation with the PLACE land surface model and variable sea surface temperature captured the basic signatures of the monsoon onset processes and associated rainfall statistics. Sensitivity tests indicated that simulations were significantly improved by including the PLACE land surface model. The mechanism by which the land surface processes affect the moisture transport and the convection during the onset of the southeast Asian monsoon were analyzed. The results indicated that land surface processes played an important role in modifying the low-level wind field over two major branches of the circulation: the southwest low-level flow over the Indo-China peninsula and the northern, cold frontal intrusion from southern China. The surface sensible and latent heat fluxes modified the low-level temperature distribution and merit, and therefore the low-level wind due to the thermal wind effect. The more realistic forcing of the sensible and latent heat fluxes from the detailed, land surface model improved the low-level wind simulation and associated moisture transport and convection.
To characterize Coronavirus Disease 2019 (COVID-19) transmission dynamics in each of the metropolitan statistical areas (MSAs) surrounding Dallas, Houston, New York City, and Phoenix in 2020 and 2021, we extended a previously reported compartmental model accounting for effects of multiple distinct periods of non-pharmaceutical interventions by adding consideration of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants Alpha (lineage B.1.1.7) and Delta (lineage B.1.617.2). For each MSA, we found region-specific parameterizations of the model using daily reports of new COVID-19 cases available from January 21, 2020 to October 31, 2021. In the process, we obtained estimates of the relative infectiousness of Alpha and Delta as well as their takeoff times in each MSA (the times at which sustained transmission began). The estimated infectiousness of Alpha ranged from 1.1x to 1.4x that of viral strains circulating in 2020 and early 2021. The estimated relative infectiousness of Delta was higher in all cases, ranging from 1.6x to 2.1x. The estimated Alpha takeoff times ranged from February 1 to February 28, 2021. The estimated Delta takeoff times ranged from June 2 to June 26, 2021. In conclusion, estimated takeoff times are consistent with genomic surveillance data.
Efforts in global heath need to deal not only with current challenges, but also to anticipate new scenarios, which sometimes unfold at lightning speed. Predictive modeling is frequently used to assist planning, but outcomes depend heavily on a subset of critical assumptions, which are mostly hampered by our limited knowledge about the many factors, mechanisms and relationships that determine the dynamics of disease systems, by a lack of data to parameterize and validate models, and by uncertainties about future scenarios. We propose a shift from a focus on the prediction of individual disease patterns to the identification and mitigation of broader fragilities in public health systems. Modeling capabilities should be used to perform “stress tests” on how interrelated fragilities respond when faced with a range of possible or plausible threats of different nature and intensity. This system should be able to reveal crosscutting solutions with the potential to address not only one threat, but multiple areas of vulnerability to future health risks. Actionable knowledge not based on a narrow subset of threats and conditions can better guide policy, build societal resilience and ensure effective prevention in an uncertain world.
We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4. The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts. Model exceedance variability was analyzed using AQMEII-4 diagnostics. Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO2 emissions reduction legislation which started in 2010 (SO2 SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations. The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha-1 d-1, and for the EU ensemble -1.15 eq ha-1 d-1. The NA daily wet deposition average mean bias for NH4+ was -0.37 eq ha-1d-1; EU -1.19 eq ha-1 d-1. The daily NA wet NO3- deposition average mean bias was -0.24 eq ha-1d-1; EU -0.69 eq ha-1 d-1. The members of the ensemble diverged (factor of 10) in their North American predictions for Ndep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average). These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH3) and -0.85 ppbv (surface NH3) compared to ensemble averages of -0.30 and -0.34 ppbv. Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models. The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m-3; ensemble bias +0.15 ug m-3). We believe two factors may have led to these latter overestimates: higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above. Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition.
The long-range goal of several past and current projects in our DOE-supported research has been the development of new and improved parameterizations of cloud-radiation effects and related processes, using ARM data, and the implementation and testing of these parameterizations in global models. The main objective of the present project being reported on here has been to develop and apply advanced statistical techniques, including Bayesian posterior estimates, to diagnose and evaluate features of both observed and simulated clouds. The research carried out under this project has been novel in two important ways. The first is that it is a key step in the development of practical stochastic cloud-radiation parameterizations, a new category of parameterizations that offers great promise for overcoming many shortcomings of conventional schemes. The second is that this work has brought powerful new tools to bear on the problem, because it has been an interdisciplinary collaboration between a meteorologist with long experience in ARM research (Somerville) and a mathematician who is an expert on a class of advanced statistical techniques that are well-suited for diagnosing model cloud simulations using ARM observations (Shen). The motivation and long-term goal underlying this work is the utilization of stochastic radiative transfer theory (Lane-Veron and Somerville, 2004; Lane et al., 2002) to develop a new class of parametric representations of cloud-radiation interactions and closely related processes for atmospheric models. The theoretical advantage of the stochastic approach is that it can accurately calculate the radiative heating rates through a broken cloud layer without requiring an exact description of the cloud geometry.
The relationship between thunderstorm activity during the summer months along coastal regions of the Atlantic and Gulf coasts and the dry sea breeze circulation was investigated. Satellite composites of thunderstorm activity for synoptically undisturbed conditions have been obtained for south Florida for a series of days in the summer of 1983. These data were catalogued into different low level synoptic flow regimes. Five synoptic flow regimes were found from the data. A three-dimensional mesoscale numerical model was used for each sysnoptic flow regime to quantitatively predict the location of enhanced thunderstorm activity. This model includes a parameterization of vegetation and soil moisture feedbacks as well as a sophisticated planetary boundary layer representation. Using the results of the satellite image composites, spatial and temporal characteristics of deep convective cloud patterns and their variation with synoptic flow are described. The results from the numerical model have provided explanations for the observed patterns.
A 3D spectral model was used to simulate the zonal mean state of the middle atmosphere for solstice and equinox conditions. The model incorporates realistic parameterizations of atmospheric infrared cooling and a gravity wave formulation based on a combination of Lindzen's (1981) and Matsuno's (1982) approaches. The temperature distributions for both seasons and the zonal wind distribution for solstice are found to be in satisfactory agreement with the empirical model of Fleming et al. (1988). Net vertical fluxes of horizontal momentum are in good agreement with systematic observations of gravity waves in the middle atmosphere.
In 1975 Gerard O Neill published in the journal Science a model for the construction of solar power satellites. He found that the solar power satellites suggested by Peter Glaser would be too massive to launch economically from Earth, but could be financially viable if the workforce was permanently located in free space habitats and if lunar and asteroid materials were used for construction. All new worldwide electrical generating capacity could be then achieved by solar power satellites. The project would financially break even in about 20 years after which it would generate substantial income selling power below fossil fuel prices. Two NASA / Stanford University led studies at Ames Research center during the summers of 1974 and 1976 found the concept technically sound and developed a detailed financial parametric model. Although the project was not undertaken when suggested in the 1970s, several contemporary issues make pursuing the O Neill -- Glaser concept more compelling today. First, our analysis suggests that if in the first ten years of construction that small habitats (compared to the large vista habitats envisioned by O Neill) supporting approximately 300 people were utilized, development costs of the program and the time for financial break even could be substantially improved. Second, the contemporary consensus is developing that carbon free energy is required to mitigate global climate change. It is estimated that 300 GW of new carbon free energy would be necessary per year to stabilize global atmospheric carbon. This is about 4 times greater energy demand than was considered by the O Neill Glaser model. Our analysis suggests that after the initial investments in lunar mining and space manufacturing and transportation, that the profit margin for producing space solar power is very high (even when selling power below fossil fuel prices). We have investigated the financial scaling of ground launched versus space derived space solar power satellites. We find that for the carbon mitigation case even modernized ground launched space solar power satellites are not financially viable. For space derived solar power satellites, however, the increased demand makes them break even substantially sooner and yield much higher profit. Third, current awareness is increasing about the dangers of humanity remaining a single planet species. Our technological power has been increasing relative to the size of the planet Earth. Since the middle of the 20th century our technological power has grown large relative to our planet's size. This presents a very real potential for human self-extinction. We argue that the potential for human self-extinction is increasing with time in proportion to the exponential growth of our technological power making self-extinction likely within this century if humanity remains a single planet species. The O Neill model of multiple independent free space habitats, it is argued, can protect humanity from extinction in the same way that portfolio diversification protects ones assets from total loss. We show that about 1 million people for the electricity only case, and about 1 billion people for the carbon mitigation case, can be provided with permanent space habitats and transportation from Earth in 30 years and can be funded by the space derived solar power satellite program. 1.2 Scope of this Chapter The goal of this chapter is to illustrate the power and importance of the O'Neill-Glaser concept in the context of human survival and maintaining a healthy planet Earth. We argue that at this point in human history our technological power is too dangerous to our selves and our home planet for us not to expand into space. We show by the models presented in the chapter that the imminent dangers of global warming and human self-extinction mandate that humanity move aggressively into the solar system in this generation. We show that the production of solar power satellites using space resources and with a work foe living in space provides a viable financial model to mitigate CO2 preventing the worst global warming scenarios, and safeguards humanity against self-extinction by providing hundreds of habitats and a billion people living in space within about 35 years. To accomplish this goal we need only consider the classic O'Neill-Glaser model which was parameterized for 1970's technological projections. Only habitat size optimization for the first ten years of production is added. This is a conservative approach since the innovations of the last 30 years will make the financial projections more favorable. However, the classic O'Neill-Glaser model represented a broad technological consensus. The model is well documented in the references and our calculations can be easily reproduced In this chapter the economics of the O Neill - Glaser model is compared with models that rely exclusively on Earth launched materials. Although many studies of Earth launched Solar Power Satellites have been made, we found that the NASA "Fresh Look Study" was the most comprehensive and well documented. It also provided one of the most optimistic Earth launch financial projections. We thus chose it for comparison purposes.
NASA’s Orbital Debris Program Office (ODPO) develops and maintains a number of modeling tools to analyze and simulate the orbital debris environment. One of the most important products produced by the NASA ODPO is the Orbital Debris Engineering Model (ORDEM). This model can be used by satellite designers and operators to design missions for better protection against the debris environment. The ODPO is currently working on the next generation, designated ORDEM 4.0. ORDEM 4.0 will include many known features from previous models, such as the ability to input a spacecraft orbit and time and the ability to compute the flux as a function of debris size, impact speed, impact direction, and debris material densities, as well as uncertainty information on the flux. A new addition will be a parameterized debris shape model based on laboratory hypervelocity impact tests, including DebriSat. ORDEM is primarily based on dedicated debris measurements, such as by the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), NASA’s Goldstone radar, and observations of geosynchronous orbits (GEO) using the Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT). In addition to ORDEM, the ODPO also maintains other models, such as the LEO-to-GEO Environment Debris (LEGEND) model for studies of long-term evolution of Earth’s debris environment, with the ability to study various mitigation and remediation strategies. Another model, the Satellite Breakup Risk Assessment Model (SBRAM), is used to analyze how satellite breakups may affect critical space missions (such as the ISS) on short notice. In addition to these models, the ODPO maintains other secondary models used to model satellite explosions and collisions, analyze radar, optical, and in situ data, and to model such things as solar activity and orbit evolution. In this presentation, a survey of these models will be presented, showing how the different models are used together to create a comprehensive picture of Earth’s debris environment.
The fundamental scientific objectives of our research are to use ARM observations and the NCAR CAM5 to understand the large-scale control on convection, and to develop improved convection and cloud parameterizations for use in GCMs.
Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.