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Climate modelling requires spending an extensive amount of time programming, which means reading, learning, testing, and evaluating source code. Fortunately, many climate models have been developed within the past decades, making it easier for climate studies to be conducted on a global scale. However, some climate models have millions of code lines, making the introduction of new parameterizations a laborious task that demands teamwork. While it is true that the high complexity models perform realistic climate simulations, some researchers perform their studies using simplified climate models in the preliminary test phases. This realisation motivated the development of the Daily INSOLation (DINSOL) model, a robust computer program to support the simplified climate models, performing solar radiation calculations while considering Milankovitch cycles and offering various simulation options for its users. DINSOL was intended to function as a model which supplies data (e.g., daily insolation, instantaneous solar radiation, orbital parameters of the Earth, and the calendar dates), such as the PMIPII. While preparing the boundary conditions of solar radiation for climate models, it was realised that the DINSOL model could also be a helpful tool for use in classrooms. Thus, it was decided that an intuitive graphical user interface would be required to cater to this educational purpose. The model was written in the Fortran 90 language, while its graphical user interface would be built using PyGTK, a Python application programming interface (API) based on GIMP ToolKit (GTK). Furthermore, the R language would also be used to generate a panel containing contour fields and sketches of the orbital parameters to support the graphical execution. The model evaluation made use of data from PMIPII and other models, and the data analysis was performed through statistical methods. Once all tests were concluded, an insignificant difference between the DINSOL-obtained results and the results obtained from other models validated the viability of DINSOL as a tool.

Short-term orbital forcing: A quasi-review and a reappraisal of realistic boundary conditions for climate modeling

This paper describes a dynamic parameterization for a simple global climate model that is based on the planetary boundary layer equations of horizontal motion. This new parameterization resolves many of the problems associated with other representations of the general circulation in simple climatic models. The model developed in this study accurately simulates the presently observed zonally‐averaged temperature and wind field patterns. Sensitivity tests demonstrate that a model with the new dynamic parameterization meets or exceeds the performance levels of other one‐dimensional energy balance global climate models. By solving many of the problems associated with the available alternative circulation parametcrizations, the new circulation representation based on the equations of motion appears to improve the quality of the popular simple climate models.

Carbon dioxide (CO2) concentrations have increased in the atmosphere as a direct result of human activity and are at their highest level over the last 2-3 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO2, the fate of anthropogenic CO2 over time, and the trend and robustness of the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model, with the novel ability to explicitly track carbon as it flows through the Earth system. We use a priori probability distribution functions for key model parameters in a Monte Carlo analysis of 10,000 coupled carbon-climate model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much CO2 ends up in the atmosphere. Unsurprisingly, we find that anthropogenic emissions are the dominant source of near- and long-term atmospheric CO2, composing roughly 45% of the atmosphere, which is consistent with observational studies of the airborne fraction. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960-2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. Furthermore, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics and destination over time of anthropogenic CO2 in the Earth system.

We will assemble and analyse instrumental climate data and to develop simple climate models as a basis for (1) detecting greenhouse-gas-induced climatic change, and (2) validation of General Circulation Models. In addition to variations in greenhouse gas concentrations, climate responds to a number of other forcing factors, changes in ocean circulation, volcanic activity, solar irradiance, etc. To detect the greenhouse effect, its signal must be isolated from the noise'' of natural climatic variability, a significant part of which is due to these other factors. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. Available land and marine data bases will updated and expanded, and differences between different data sets will be reconciled where possible. The data will be analysed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided by simple energy balance climate models. These analyses are oriented toward obtaining early evidence of greenhouse-gas-induced climatic change that would lead either to confirmation, rejection or modification of model projections, and toward the statistical validation of General Circulation Model control runs. 23 refs., 4 figs.

MotivationAstrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood–brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations.ResultsWe present an unconditionally robust method capable of reconstructing high fidelity polygonal meshes of astroglial cells from algorithmically-synthesized morphologies. Our method uses implicit surfaces, or metaballs, to skin the different structural components of astrocytes and then blend them in a seamless fashion. We also provide an end-to-end pipeline to produce optimized two- and three-dimensional meshes for visual analytics and simulations, respectively. The performance of our pipeline has been assessed with a group of 5000 astroglial morphologies and the geometric metrics of the resulting meshes are evaluated. The usability of the meshes is then demonstrated with different use cases.Availability and implementationOur metaball skinning algorithm is implemented in Blender 2.82 relying on its Python API (Application Programming Interface). To make it accessible to computational biologists and neuroscientists, the implementation has been integrated into NeuroMorphoVis, an open source and domain specific package that is primarily designed for neuronal morphology visualization and meshing.Supplementary informationSupplementary data are available at Bioinformatics online.

Breast cancer is a major health issue for women all over the world. Effective care and better patient outcomes depend on early identification and precise risk prediction. Ensemble learning algorithms and merging large data have recently emerged as potential ways to predict and classify breast cancer risk. This paper aims to investigate the feasibility of using ensemble learning and big data fusion for risk prediction and categorization of breast cancer. The key focus is building a reliable and precise model by combining the best features of various learning algorithms and a variety of vast and varied data sets. This research presents an Improved XGBoost Ensembling (I-XGBoost) technique with big data analytics to predict and diagnose breast cancer cells. The proposed work considers three critical identification phases to achieve greater accuracy: data pre-processing, feature extraction, and target role. The Wisconsin breast cancer diagnostic data was used for testing the proposed method. It is also compared in terms of performance with other classification methods like Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, Adaboost, and XGBoost. This research aims to determine which characteristics are most beneficial in predicting cancer as malignant or benign. We show that I-XGBoost has an impressively high accuracy score of 99.84% using Spark’s Python Application Programming Interface (API). This research emphasizes the promise of ensemble learning and big data fusion for predicting and classifying breast cancer risks. By proposing a new method that draws on the best features of several models and uses the wealth of information available in big data, it adds to the current body of knowledge. Positive findings highlight the practical use of the suggested paradigm in clinical contexts.

Design and implementation of a general batch simulation tool of SOWFA and its application in training a single-turbine surrogate

Modern high-performance computing (HPC) tasks overwhelm conventional geophysical data formats. We describe a new data schema called HDF5eis (read H-D-F-size) for handling big multidimensional time series data from environmental sensors in HPC applications and implement a freely available Python application programming interface (API) for building and processing HDF5eis files. HDF5eis augments the popular Hierarchical Data Format 5 with a minimal set of additional conventions that facilitate fast and flexible data input and output protocols for regularly sampled (in time) data with any number of dimensions. HDF5eis supports arbitrary ancillary data (e.g., metadata) storage in columnar format or as UTF-8 encoded byte streams alongside time series data. Our HDF5eis API enables simple and efficient access to big data sets distributed across a potentially large number of small heterogeneous files through a single point of access. HDF5eis outperforms conventional seismic data formats by up to two orders of magnitude in terms of random read access times. We contribute HDF5eis as an operational tool and an experimental draft proposal that will help establish the next generation of data standards in the earth sciences.

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the "natural" and "cultural" landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. This research represents one of the first applications of the Google Earth Engine (GEE) Python application programming interface (API) in studies of historic landscapes. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab, which could be adapted and replicated in different areas of the world. A multi-temporal approach has been adopted to investigate the potential of Sentinel-2 satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. The protocol's effectiveness in identifying palaeo-riverscape features has been tested in the Po Plain (N Italy).

BackgroundSynthetic biology brings together concepts and techniques from engineering and biology. In this field, computer-aided design (CAD) is necessary in order to bridge the gap between computational modeling and biological data. Using a CAD application, it would be possible to construct models using available biological "parts" and directly generate the DNA sequence that represents the model, thus increasing the efficiency of design and construction of synthetic networks.ResultsAn application named TinkerCell has been developed in order to serve as a CAD tool for synthetic biology. TinkerCell is a visual modeling tool that supports a hierarchy of biological parts. Each part in this hierarchy consists of a set of attributes that define the part, such as sequence or rate constants. Models that are constructed using these parts can be analyzed using various third-party C and Python programs that are hosted by TinkerCell via an extensive C and Python application programming interface (API). TinkerCell supports the notion of a module, which are networks with interfaces. Such modules can be connected to each other, forming larger modular networks. TinkerCell is a free and open-source project under the Berkeley Software Distribution license. Downloads, documentation, and tutorials are available at .ConclusionAn ideal CAD application for engineering biological systems would provide features such as: building and simulating networks, analyzing robustness of networks, and searching databases for components that meet the design criteria. At the current state of synthetic biology, there are no established methods for measuring robustness or identifying components that fit a design. The same is true for databases of biological parts. TinkerCell's flexible modeling framework allows it to cope with changes in the field. Such changes may involve the way parts are characterized or the way synthetic networks are modeled and analyzed computationally. TinkerCell can readily accept third-party algorithms, allowing it to serve as a platform for testing different methods relevant to synthetic biology.

<p>The quantification of Earth’s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of  temporal changes in the solar radiative components of the climate system.</p> <p>We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm<sup>-2</sup> globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model  generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).</p> <p>With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.</p>

La búsqueda de información a través de la World Wide Web es una tarea común hoy en día. Sin embargo, es sobre todo una experiencia textual. Este trabajo propone e implementa un entorno 3D para la representación de los resultados de búsqueda Web en un entorno en tres dimensiones (3D) que es controlada por gestos y reconocimiento de voz. Con el fin de hacer eso, se requieren dos escenarios: una solución de servidor basado en Servicios Web XML que permite ser consumidos por los clientes que están buscando información en la Web; y una aplicación en cliente que permite ser ejecutado como una interfaz gráfica de usuario en el escritorio mediante el uso de comandos de voz y gestos. Esta última aplicación se encarga de modelar gráficamente el entorno 3D de acuerdo con los resultados de búsqueda Web. Un prototipo del sistema propuesto fue implementado, lo que representa un conjunto de resultados de búsqueda Web en un entorno 3D. El escenario 3D es manejada por gestos y comandos de voz; el entorno 3D se basa en la administración de algunas transformaciones 3D tales como la rotación y el escalado de las caras interiores de un toroide. Una evaluación del prototipo en contextos de búsqueda sugiere que hay un alto nivel de aceptación del usuario del modelo multimedia propuesto. Las personas que probaron el prototipo han dicho que tiene una interfaz gráfica de usuario intuitiva, y que tanto el habla como el reconocimiento de gestos han sido una alternativa altamente funcional para las tareas relacionadas con la búsqueda Web. Sin embargo, hay algunos aspectos a mejorar, sobre todo en el apoyo de reconocimiento de voz de diferentes acentos. En general, el prototipo multimedia cumplió con las expectativas de una muestra de los usuarios finales, lo que demuestra que este tipo de interfaces tienen un gran potencial.

Water vapour plays a key role in the Earth's energy balance. Almost 50% of the absorbed solar radiation at the surface is used to cool the surface, through evaporation, and warm the atmosphere, through release of latent heat. Latent heat is the single largest factor in warming the atmosphere and in transporting heat from low to high latitudes. Water vapour is also the dominant greenhouse gas and contributes to a warming of the climate system by some 24°C (Kondratev 1972). However, water vapour is a passive component in the troposphere as it is uniquely determined by temperature and should therefore be seen as a part of the climate feedback system. In this short overview, we will first describe the water on planet Earth and the role of the hydrological cycle: the way water vapour is transported between oceans and continents and the return of water via rivers to the oceans. Generally water vapour is well observed and analysed; however, there are considerable obstacles to observing precipitation, in particular over the oceans. The response of the hydrological cycle to global warming is far reaching. Because different physical processes control the change in water vapour and evaporation/precipitation, this leads to a more extreme distribution of precipitation making, in general, wet areas wetter and dry areas dryer. Another consequence is a transition towards more intense precipitation. It is to be expected that the changes in the hydrological cycle as a consequence of climate warming may be more severe that the temperature changes.

Coupled climate model simulations designed to isolate the effects of Arctic sea ice loss often apply artificial heating, either directly to the ice or through modification of the surface albedo, to constrain sea ice in the absence of other forcings. Recent work has shown that this approach may lead to an overestimation of the climate response to sea ice loss. In this study, we assess the spurious impacts of ice-constraining methods on the climate of an idealized aquaplanet general circulation model (GCM) with thermodynamic sea ice. The true effect of sea ice loss in this model is isolated by inducing ice loss through reduction of the freezing point of water, which does not require additional energy input. We compare the results of freezing point modification experiments with experiments where sea ice loss is induced using traditional ice-constraining methods and confirm the result of previous work that traditional methods induce spurious additional warming. Furthermore, additional warming leads to an overestimation of the circulation response to sea ice loss, which involves a weakening of the zonal wind and storm-track activity in midlatitudes. Our results suggest that coupled model simulations with constrained sea ice should be treated with caution, especially in boreal summer, where the true effect of sea ice loss is weakest, but we find the largest spurious response. Given that our results may be sensitive to the simplicity of the model we use, we suggest that devising methods to quantify the spurious effects of ice-constraining methods in more sophisticated models should be an urgent priority for future work. Significance Statement The potential effects of Arctic sea ice loss on midlatitude weather have been investigated using climate models where sea ice loss is artificially induced, without increasing greenhouse gas concentrations. Recently, this approach has been challenged because the artificial heating used to melt sea ice may also have direct effects on climate, which are not caused by sea ice loss. We run simulations with a simplified climate model that allows us to separate the “spurious” direct effects of the artificial heating from the “true” effect of sea ice loss. In our simulations, the responses of temperature and atmospheric circulation are amplified by spurious effects. Consequently, we argue that previous studies using more complex climate models may overestimate the effect of sea ice loss on climate.