Search

In this study, the relationship between AO and winter synoptic temperature variability (STV) over the Northern Hemisphere is examined in 34 CMIP5/CMIP6 model outputs. With significant model bias around the North Pacific and North Atlantic, most models fail to capture the correct AO–STV pattern in historical simulations compared to observations. To investigate the bias of AO–STV relationship simulations, AO-related processes for the connection between AO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. Furthermore, the bias of AO impact can be traced back to AO pattern simulations. On the one hand, compared to observations, HPS models can overall capture the intensity in the North Pacific and North Atlantic center of AO. On the other hand, LPS models tend to overestimate the North Pacific center and underestimate the North Atlantic center. In addition, similar to historical simulations, a robust AO–STV relationship can still be found over the Northern Hemisphere in future projections based on HPS models. Meanwhile, the uncertainty of the projected AO–STV relationship in the multimodel ensemble is confined mainly to the North Pacific, consistent with the large diversity of intensity over the North Pacific center of AO, which is related to the uncertainty of the relationship between AO and regional mode variability.

Precise and robust localization is critical for many navigation tasks, especially autonomous driving systems. The most popular localization approach is global navigation satellite systems (GNSS). However, it has several shortcomings such as multipath and nonline- of-sight reception. Vision-based localization is one of the approaches without using GNSS which is based on vision. This paper used visual localization with a prior 3D LiDAR map. Compared to common methods for visual localization using camera-acquired maps, this paper used the method that tracks the image feature and poses of a monocular camera to match with prior 3D LiDAR maps. This paper reconstructs the image feature to several sets of 3D points by a local bundle adjustment-based visual odometry system. Those 3D points matched with the prior 3D point cloud map to track the globe pose of the user. The visual localization approach has several advantages. (1) Since it only relies on matching geometry, it is robust to changes in ambient luminosity appearance. (2) Also, it uses the prior 3D map to provide viewpoint invariance. Moreover, the proposed method only requires users to use low-cost and lightweight camera sensors.

This study investigates bidecadal variation and trends in the winter East Asian trough (EAT) intensity for the period from 1958 to 2020. The EAT intensity index, calculated with the JRA55 reanalysis, demonstrates bidecadal variation, which is closely related to the intensity of cold advection in East Asia that affects northeast China, the Korean Peninsula, and Japan. In addition, it is noted that the positive phase of the Arctic Oscillation (AO) plays an important role in suppressing EAT intensity, particularly on a bidecadal time scale. On the other hand, a warm anomaly in the west Pacific can enhance the intensity of the EAT, which is also reproduced by numerical simulation. The influence of a significant warming trend in the west Pacific and a weak positive trend in the AO on the EAT intensity and the East Asian winter monsoon is investigated. Warming in the west Pacific supports a strengthening of EAT intensity, cooling in northeast Asia, and enhancement of easterly wind in the subtropical west Pacific. A weak positive AO trend plays an insignificant role in affecting the trend of EAT intensity, but it supports warming in northeast Asia.

Although the relationship between the environmental factors such as weather conditions and air pollution and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed.We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone,NO2,SO2,CO, PM10andPM2.5) using data between March 26, 2020 and May 12, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria (AIC) and the closeness between the predicted and observed values of CFR.We found that the low temperature (8-11°C), prolonged sunlight duration (11-13hours) and increasedPM2.5(11-18μg/m3) after the incubation period posed a greater risk of death (measured by odds ratio (OR)) than the earlier infectious stages. The risk reached its maximum level when the low temperature occurred one day after (OR = 1.76; 95% CI: 1.10-2.81), prolonged sunlight duration 2-3 days after (OR = 1.50; 95% CI: 1.03-2.18) and increasedP.M2.5at the onset of symptom (OR =1.72; 95% CI: 1.30-2.26). In contrast, prolonged sunlight duration showed a protective effect during the incubation period or earlier.After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

This study focuses on the temporal variation of dissimilarity in heat content (HC) anomalies in the upper 300 m of ocean (HC300A) in the equatorial Pacific (±10°N) and its response to the El Nino-Southern Oscillation (ENSO). The HC300A anomalies are derived from four ocean reanalyses that are commonly used in ENSO studies and are compared using a simple differencing method. The dissimilarity in HC300A is found to vary closely with the magnitude of ENSO (regardless of phase), meaning that it tends to be greater during strong ENSO events. However, the dissimilarity among ocean reanalyses persists after the event decays. This effect is more pronounced after strong events. The persistence of the dissimilarity after ENSO events is a result of a late maturation of the ENSO signal, its persistence, and the interruption of the signal decay due to follow-up ENSO events. The combined effect of these three factors slows down the decay of HC300A in the region and hence results in the slow decay of dissimilarity. It is also found that areas with a significant spread in vertical temperature profiles collocate with the ENSO signal during warm ENSO phases. Thus, differences in subsurface process reconstruction are a significant factor in the dissimilarity among ocean reanalyses during warm ENSO events.

Synoptic eddies (with a period of two to eight days) are active in the South China Sea-Philippine Sea (SCS-PS) and control weather variations. In addition, the intensity and frequency of synoptic eddies may change along with variations in sea surface temperatures (SST). This paper presented the influence of SST in the northwest Indian Ocean on synoptic eddies in the lower troposphere over the SCS-PS in June. Our statistical analysis showed a significant negative correlation between the SST in the northwest Indian Ocean and the synoptic scale eddy kinetic energy (EKE) in the SCS-PS. By analyzing the EKE budget of synoptic eddies, we found that the variation in the synoptic scale EKE over the SCS-PS is mainly due to the change in the monthly zonal wind gradient, which affects the barotropic energy conversion between the monthly mean flow and the synoptic eddies. Additionally, the northwest Indian Ocean SST modulates the monthly flow over the SCS-PS by alternating the strength of the Walker circulation in the west Pacific and Indian Ocean. Finally, the influence of SST in the northwest Indian Ocean on EKE in the SCS-PS was reproduced using the simplified atmospheric general circulation model, SPEEDY.

In this study, the relationship between ENSO and winter synoptic temperature variability (STV) over the Asian-Pacific-American region is examined in 26 CMIP5/6 model outputs. Compared to observations, most models fail to simulate the correct ENSO-STV relationship in historical simulations. To investigate the possible bias in the ENSO-STV simulations, two possible processes for the connection between ENSO and winter STV are examined in high pattern score (HPS) models and low pattern score (LPS) models, respectively. On the one hand, both HPS and LPS models can overall reproduce a reasonable relationship between STV and the mean-flow conditions supporting extratropical eddy development. On the other hand, only HPS models can well capture the relationship between ENSO and the development of extratropical eddies, while LPS models fail to simulate this feature, indicating that the bias in the simulated ENSO-STV relationship among CMIP5/6 models can be traced back to ENSO simulation. Furthermore, the bias of the ENSO simulation is characterized by an unreasonable SST pattern bias, with an excessive westward extension of warm SST anomalies over the western Pacific and weak warm SST anomalies over the equatorial central-eastern Pacific, resulting in the underestimation of the zonal SST anomaly gradient among models. Therefore, the ENSO pattern bias induces an unrealistic circulation and temperature gradient over the Asian-Pacific-American region, affecting the simulations of the ENSO-STV connection. In addition, the ENSO-STV relationship over the Asian-Pacific-American region is still robust in future projections based on HPS models, providing implications for the selection of future climate predictors.