Hydrodeoxygenation (HDO) of waste cooking oil and trapped grease over sulfide catalysts was examined to produce high quality transportation fuel from low-grade resources. The hydrodeoxygenation of ...waste oils was carried out in a high pressure batch reactor and a fixed bed flow reactor. Sulfide catalysts showed high HDO activity and all waste oils gave n-paraffins, isoparaffins and small amount of olefins. NiMo and NiW catalysts showed high and stable hydrogenation activity, whereas the deactivation of hydrogenation activity was observed using CoMo catalyst. NiW catalyst gave more hydrocarbons formed by decarboxylation or decarbonylation than NiMo and CoMo catalysts. The sulfur content in the product oil was low when catalytic activity showed constant.
Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against ...observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.
The tropical Pacific cooling from the early 1990s to 2013 has contributed to the slowdown of globally averaged sea surface temperatures (SSTs). The origin of this regional cooling trend still remains ...elusive. Here we demonstrate that the remote impact of Atlantic SST anomalies, as well as local atmosphere‐ocean interactions, contributed to the eastern Pacific cooling during this period. By assimilating observed three‐dimensional Atlantic temperature and salinity anomalies into a coupled general circulation model, we are able to qualitatively reproduce the observed Pacific decadal trends of SST and sea level pressure (SLP), albeit with reduced amplitude. Although a major part of the Pacific SLP trend can be explained by equatorial Pacific SST forcing only, the origin of this low‐frequency variability can be traced back further to the remote impacts of equatorial Atlantic and South Atlantic SST trends. Atlantic SST impacts on the atmospheric circulation can also be detected for the Northeastern Pacific, thus providing a linkage between Atlantic climate and Western North American drought conditions.
Key Points
Atlantic SST anomalies contribute to generation of Pacific decadal variability
Equatorial and South Atlantic SST trends reorganize global Walker Circulation
Phase of Interdecadal Pacific Oscillation influenced by SST anomalies in other ocean basins
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change
. Although inter-model agreement is high for ...large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain
. This leads to low confidence in regional projections, especially for precipitation, over the coming decades
. The chaotic nature of the climate system
may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models
, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.
Robust skill of decadal climate predictions Smith, D. M.; Eade, R.; Scaife, A. A. ...
NPJ climate and atmospheric science,
05/2019, Letnik:
2, Številka:
1
Journal Article, Publication
Recenzirano
Odprti dostop
Abstract
There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of ...precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change.
To evaluate the effect of augmented training datasets in a deep convolutional neural network (DCNN) used for detecting abnormal chest radiographs.
Chest radiographs were corrected to conform to a ...DCNN dataset, with 288 abnormal and 447 normal radiographs. The radiographic images were divided into training and validation sets (441, 60%), and a test set (294, 40%). The training and validation sets were augmented to generate a total of 12,789 training and validation images. The augmentation consisted of operations such as rotation, horizontal and vertical flipping, Gaussian blur, and brightness variation, either alone or combined. The DCNN performed binary classification of the images as being abnormal or normal chest radiographs, and accuracy was used as measure to assess the model performance.
The accuracy of the DCNN trained with the augmented dataset tended to be higher than that of the DCNN trained with the non-augmented dataset. The augmented datasets combining rotation and horizontal flipping had a high accuracy of 0.91, showing the highest accuracy among the applied augmentation techniques and combinations.
Augmentation of training datasets can improve the performance of DCNN for radiographic image classification depending on the applied augmentation technique.
•Deep convolutional neural networks can be more accurate using augmented datasets.•Different augmentation techniques affect the network classification performance.•Augmentation using rotation and horizontal flipping retrieved the highest accuracy.•Augmentation prevents the correction of large datasets for improving performance.•Augmentation enables large sets, especially for the scarce clinical imaging data.
Hsp70: J-domain protein (JDP) machines, along with the cellular protein degradation systems play a central role in regulating cellular proteostasis. An equally robust surveillance system operates at ...the plasma membrane too that affects proper sorting, stability as well as the turnover of membrane proteins. Although plausible, a definitive role of the Hsp70: JDP machine in regulating the stability of plasma membrane proteins is not well understood in Saccharomyces cerevisiae. Here we show that a moderate over-expression of Caj1, one of the thirteen JDPs residing in the nucleo-cytosolic compartment of S. cerevisiae reduced the cold sensitivity of tryptophan auxotrophic yeast cells by stabilizing tryptophan permeases, Tat1 and Tat2 in a J-domain dependent manner. Concomitantly, higher Caj1 levels also caused slow growth and increased plasma membrane damage at elevated temperatures possibly due to the stabilization of thermolabile plasma membrane proteins. Finally, we show that although majorly cytosolic, Caj1 also co-localizes with the membrane dye FM4-64 at the cellular periphery suggesting that Caj1 might interact with the plasma membrane. Based on the results presented in this study, we implicate the Hsp70: Caj1 chaperone machine in regulating the stability or turnover of plasma membrane proteins in budding yeast.
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•Caj1 is the first J-domain protein to associate with plasma membrane.•Caj1 overexpression rescues cold sensitivity of Trp1 auxotrophic strains and causes toxicity at elevated temperatures.•Caj1 overexpression stabilizes amino acid permeases possibly by negatively regulating Rsp5-mediated protein degradation.•Caj1 may have both J-domain dependent as well as J-domain independent functions.
Recent developments in imaging technology have enabled CT and MR cholangiopancreatography (MRCP) to provide minimally invasive alternatives to endoscopic retrograde cholangiopancreatography for the ...pre- and post-operative assessment of biliary disease. This article describes anatomical variants of the biliary tree with surgical significance, followed by comparison of CT and MR cholangiographies. Drip infusion cholangiography with CT (DIC-CT) enables high-resolution three-dimensional anatomical representation of very small bile ducts (e.g. aberrant branches, the caudate branch and the cystic duct), which are potential causes of surgical complications. The disadvantages of DIC-CT include the possibility of adverse reactions to biliary contrast media and insufficient depiction of bile ducts caused by liver dysfunction or obstructive jaundice. Conventional MRCP is a standard, non-invasive method for evaluating the biliary tree. MRCP provides useful information, especially regarding the extrahepatic bile ducts and dilated intrahepatic bile ducts. Gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced MRCP may facilitate the evaluation of biliary structure and excretory function. Understanding the characteristics of each type of cholangiography is important to ensure sufficient perioperative evaluation of the biliary system.
The El Niño–Southern Oscillation (ENSO) exerts a strong influence on tropical Atlantic variability, but it is also affected by Atlantic forcing. Previous research has proposed three Atlantic ...precursors for ENSO: the North tropical Atlantic, the equatorial Atlantic, and the entire tropical Atlantic. However, the relative importance of these Atlantic precursors for ENSO remains unclear. Here, we present evidence from a set of multimodel partial ocean assimilation experiments that equatorial Atlantic cooling is the main contributor for weakening equatorial zonal winds in the Indo‐Pacific sector and subsequent ocean warming in the tropical Pacific. Opposite tendencies occur for a warmer equatorial Atlantic. The equatorial Atlantic affects the interbasin climate seesaw between the Atlantic and Pacific through an atmospheric zonal Wavenumber 1 pattern. However, model mean state biases and systematic errors prevent a precise assessment of the response times for the equatorial Pacific trade winds to Atlantic forcing.
Plain Language Summary
El Niño—an unusual surface warming of the tropical Pacific—may be more predictable than previously thought if the prediction of Atlantic climate, and its remote impact on the Indo‐Pacific region can be improved. In this study, we found that sea surface cooling in the equatorial Atlantic weakens western Pacific trade winds and triggers subsequent tropical Pacific warming through a positive feedback of atmosphere‐ocean interactions. This process increases the chance of an El Niño event 7 months later. By assimilating observed ocean data in this simulation, we found that El Niño predictive skill relies not only on the tropical Pacific climate state but also on the Atlantic mean state and its remote impact on the tropical Pacific. Our result suggests that improving model performance in the Atlantic ocean and its remote impacts are crucial for enhancing El Niño predictions.
Key Points
Model runs show that equatorial Atlantic warming (cooling) triggers subsequent tropical Pacific cooling (warming) 7 months later
Pacific wind‐SST feedbacks are robust on ENSO timescales, but model sensitivity is large in Pacific wind response to Atlantic forcing
El Niño–Southern Oscillation predictability is modulated by the Atlantic mean state bias and systematic errors in interbasin interactions
The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including eight typhoons/hurricanes. This activity was successfully predicted up to 5 months in ...advance by the Geophysical Fluid Dynamics Laboratory Forecast‐Oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, Nonhydrostatic Icosahedral Atmospheric Model, and Meteorological Research Institute Atmospheric General Circulation Model) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and Meteorological Research Institute Atmospheric General Circulation Model). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.
Plain Language Summary
The potential causes of the active 2018 tropical cyclone season in the North Pacific were explored with a series of high‐resolution climate model experiments, revealing that this active cyclone season was mainly induced by subtropical Pacific warming and secondarily by tropical Pacific warming. The possible effect of anthropogenic forcing on the active cyclone season was also investigated. However, diverse results were obtained regarding projected changes in storm frequency in the case of an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty in the possible impact of anthropogenic forcing on the 2018 active cyclone season.
Key Points
The 2018 active tropical cyclone season in the North Pacific was successfully predicted 5 months in advance
The subtropical Pacific warming in 2018 mainly caused the active storm season in the North Pacific
The potential effect of anthropogenic forcing on the 2018 active storm season is uncertain
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