Seeking natural analogs to fast-forward the assessment of marine CO2 removal

2021 ◽  
Vol 118 (40) ◽  
pp. e2106147118
Author(s):  
Lennart T. Bach ◽  
Philip W. Boyd
Keyword(s):  
Real World ◽  
Large Scale ◽  
Global Climate ◽  
Experimental Studies ◽  
Small Scale ◽  
Co2 Removal ◽  
Natural Analog ◽  
Reality Check ◽  
Social License ◽  
Equatorial Upwelling

Mitigating global climate change will require gigaton-scale carbon dioxide removal (CDR) as a supplement to rapid emissions reduction. The oceans cover 71% of the Earth surface and have the potential to provide much of the required CDR. However, none of the proposed marine CDR (mCDR) methods is sufficiently well understood to determine their real-world efficiency and environmental side effects. Here, we argue that using natural mCDR analogs should become the third interconnecting pillar in the mCDR assessment as they bridge the gap between numerical simulations (i.e., large scale/reduced complexity) and experimental studies (i.e., small scale/high complexity). Natural mCDR analogs occur at no cost, can provide a wealth of data to inform mCDR, and do not require legal permission or social license for their study. We propose four simple criteria to identify particularly useful analogs: 1) large scale, 2) abruptness of perturbation, 3) availability of unperturbed control sites, and 4) reoccurrence. Based on these criteria, we highlight four examples: 1) equatorial upwelling as a natural analog for artificial upwelling, 2) downstream of Kerguelen Island for ocean iron fertilization, 3) the Black and Caspian Seas for ocean alkalinity enhancement, and 4) the Great Atlantic Sargassum Belt for ocean afforestation. These natural analogs provide a reality check for experimental assessments and numerical modeling of mCDR. Ultimately, projections of mCDR efficacy and sustainability supported by observations from natural analogs will provide the real-world context for the public debate and will facilitate political decisions on mCDR implementation. We anticipate that a rigorous investigation of natural analogs will fast-forward the urgently needed assessment of mCDR.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

scholarly journals Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2868
Author(s):  
Wenxuan Zhao ◽  
Yaqin Zhao ◽  
Liqi Feng ◽  
Jiaxi Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Large Scale ◽  
Learning Strategy ◽  
Contextual Information ◽  
Small Scale ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

Dynamics of a Large Scale Hydraulic Capsule Pipeline System

1999 ◽  
Vol 124 (1) ◽  
pp. 191-195 ◽  
Author(s):  
Hongliu Du ◽  
Satish S. Nair
Keyword(s):  
Large Scale ◽  
Control Method ◽  
Experimental Studies ◽  
Small Scale ◽  
Pipeline System ◽  
Centrifugal Pumps ◽  
Long Distance ◽  
Column Separation ◽  
In Series ◽  
Hydraulic Capsule Pipeline

The dynamics of a booster station, which is critical for the control of a novel, long distance, hydraulic capsule pipeline, is simulated mathematically for design studies and control of the hydraulic transients caused by the valve actuators in the system. Several modifications to the pump bypass station configuration of the booster station have been studied. With the objective of eliminating column separation and reducing flow reversals, a configuration with several centrifugal pumps connected in series, and a carefully sized air chamber is found to be a viable design. A valve control method is designed to eliminate column separation and the design results in acceptable flow reversal levels in the main pipe. The simulation results match with trends in limited experimental studies performed on a small scale experimental capsule pipeline system.

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

scholarly journals Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (8) ◽  
pp. 3601-3610 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

INVESTIGATION INTO THE INFLUENCE OF IMPREGNATION ON PINE TIMBER COMBUSTION USING A CONE CALORIMETER AND LARGE SCALE TESTS

2011 ◽  
Vol 3 (3) ◽  
pp. 91-104 ◽  
Author(s):  
Jerzy Gałaj ◽  
Zbignev Karpovič ◽  
Waldemar Jaskółowski
Keyword(s):  
Cone Calorimeter ◽  
Large Scale ◽  
Experimental Studies ◽  
Fire Retardant ◽  
Full Scale ◽  
Lower Amount ◽  
Small Scale ◽  
Fire Retardants ◽  
Co Concentration ◽  
Piloted Ignition

Fire safety is one of the main requirements with respect to the regulations on the buildings involved into the category of human hazards. Human safety measures are applied equally to inhabitants, users, customers, spectators, etc., as well as to fire brigades taking part in the activities connected with rescue actions. Methods for emission research were applied to estimate thermo-kinetic parameters related to smoke and toxic gases. The methods fall into two types: full scale methods reflect fire conditions and small laboratory scale methods having a significantly lower degree of reflection. This paper presents the results of studies on the influence of BAK-1 and Flamasepas-2 fire retardants produced in Lithuania and applied for timber on the selected parameters of the fire environment. Experimental studies were conducted using a cone calorimeter (small scale) in the closed compartment equipped with measuring devices (full scale). The undertaken studies have demonstrated that considering some parameters, such as heat release rate (HRR), a positive effect of the lower amount of the released heat can be obtained. Unfortunately, in case of the major part of the studied parameters, including time to ignition, CO concentration and extinction parameter reflecting smokiness, worse results (shorter time, higher CO values and higher extinction coefficient) have been observed for the treated timber rather than for the non-impregnated one. The obtained results have showed combustion with piloted ignition. In case of no piloted ignition, the results were slightly different. For all studied specimens treated with fire retardants, no ignition was observed and tests were terminated following 15 minutes. CO concentration and extinction parameter (smokiness) were higher for non-impregnated timber. Full scale experiments have confirmed the above provided information; moreover, it has been found that the application of fire retardant has no significant impact on temperatures in the compartment.

scholarly journals A Hybrid Downscaling Approach for Future Temperature and Precipitation Change

2020 ◽  
Vol 59 (11) ◽  
pp. 1793-1807 ◽  
Author(s):  
Helene Birkelund Erlandsen ◽  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Marie Pontoppidan
Keyword(s):  
Regional Climate Model ◽  
Statistical Models ◽  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Small Scale ◽  
Regional Climate Model Output

AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.

scholarly journals DIMPLE GENERATOR OF VORTEX STRUCTURES

2021 ◽  
pp. 95-102
Author(s):  
A. V. Voskobijnyk ◽  
V. M. Turick ◽  
O. A. Voskoboinyk ◽  
V. A. Voskoboinick
Keyword(s):  
Large Scale ◽  
Vortex Flow ◽  
Flat Surface ◽  
Pressure Fluctuations ◽  
Experimental Studies ◽  
Vortex Structures ◽  
Small Scale ◽  
Wall Pressure Fluctuations ◽  
Large Scale Vortex ◽  
Streamlined Surface

The paper presents the results of experimental studies of the space-time characteristics of the velocity and pressure field inside a hemispherical dimple on a flat surface. The features of the formation and development of vortex structures generated inside the dimple, as well as their interaction with the streamlined surface of the dimple and the boundary layer were established. Integral, spectral and correlation characteristics of the field of velocity, dynamic and wall pressure fluctuations were obtained. The velocities and directions of transfer of large-scale vortex structures and small-scale vortices inside the dimple were determined. The frequencies of rotations and ejections of large-scale vortices, the frequencies of oscillations of the vortex flow inside the dimple and self-oscillations of the vortex structures of the shear layer, their subharmonics and harmonics of higher orders were established.

Compressive Behavior of Large-Scale Hybrid FRP-Concrete-Steel Double-Skin Tubular Columns

2011 ◽  
Vol 243-249 ◽  
pp. 1138-1144 ◽  
Author(s):  
Pan Xie ◽  
T Yu ◽  
Y.L. Wong ◽  
J.G. Teng
Keyword(s):  
Large Scale ◽  
Experimental Studies ◽  
Steel Tube ◽  
Small Scale ◽  
Structural Members ◽  
Compression Tests ◽  
Tubular Columns ◽  
Compressive Response ◽  
Double Skin ◽  
Frp Tube

Hybrid FRP-concrete-steel double-skin tubular columns (DSTCs) are a new form of hybrid structural members. A hybrid DTSC consists of an inner steel tube, an outer FRP tube and a concrete infill between them. Hybrid DSTCs possess many important advantages over conventional structural members, including their excellent corrosion resistance as well as excellent ductility and seismic resistance. A large amount of research has been conducted on hybrid DSTCs, but the existing experimental studies have been limited to the testing of small-scale columns. This paper presents preliminary results from the first series of large-scale axial compression tests on hybrid DSTCs, which forms part of a larger experimental study currently under way at The Hong Kong Polytechnic University. These test results confirm the excellent axial compressive response of hybrid DSTCs as initially expected.

scholarly journals Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

2019 ◽  
Vol 9 (13) ◽  
pp. 2634 ◽  
Author(s):  
Ok ◽  
Lee ◽  
Kim
Keyword(s):  
Random Walk ◽  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
Online Shopping ◽  
Experimental Studies ◽  
Empirical Evaluation ◽  
Personalized Recommendation ◽  
User Interests ◽  
Sparsity Problem

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

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