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Black carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long‐term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2‐MM, NorESM2‐LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi‐model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations. Plain Language Summary: Black carbon in snow (BCS) has important implications for snow albedo reduction, solar radiation absorption, and snow melt acceleration on the Earth. However, model performance in BCS simulation remains unclear in the widely used Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6). In this study, CMIP5 and CMIP6 models providing BCS information were evaluated against a global data set of BCS observations from ice cores and snow samples. Results indicate that both CMIP generations successfully reproduce long‐term trends in BCS but fail to capture the decadal variability. Marked differences exist in modeled BCS concentrations, with the best‐performing models (NorESM2‐LM, NorESM2‐MM, and TaiESM1) overestimating concentrations by <50%, while the three worst‐performing MIROC models yield extreme overestimation. Such discrepancies stem primarily from the post‐depositional treatment of black carbon in snow, which influences the estimation of climate‐warming effects of BCS. Key Points: The global pattern of black carbon in snow (BCS) is effectively simulated by CMIP5/6 models, but large errors remainPost‐depositional snow processes contribute most to the large simulated‐BCS errorsBCS‐induced radiative forcing in Antarctica is poorly simulated by CMIP5/6 models with an overestimation of ∼200,000% and 144%, respectively [ABSTRACT FROM AUTHOR]

Statistical gene detection plays an important role in biostatistics and bioinformatics. So far, many gene loci associated with human complex disease have been found by statistical methods. However, it is difficult to find all the mutation genes that are associated with a certain disease. Researchers need to detect more associated genes aiming at a disease so that human will conquer the disease one day. In this paper, we considered a real and big data set and study the detection problem of genes associated with the PIK3C2B gene on lung disease. 168 significant genes associated with the PIK3C2B gene were detected at nominal significance level 0.001 by using statistical multiple testing method. The detected genes will provide some reference to further study the function of the PIK3C2B gene to lung disease for biologists and medical scientists.

The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km 2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m -2), 0.088–0.136 (31–49 W m -2), and 0.092–0.153 (22–38 W m -2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening. [ABSTRACT FROM AUTHOR]

Under the background of implementing green travel in response to the national energy conservation and emission reduction policy, the concept of shared travel as a new transportation mode has been promoted. This paper aims at providing a more scientific and quantitative method to explore the shared travel traffic mode and evaluating efficiency with data mining technology. Based on the analysis of the evaluation index system of the existing intelligent shared mobility, this paper firstly points out some issues in reflecting the operation efficiency of a specific shared mobility service. In order to find out the characteristics of passenger flow and propose recommend indicators, this study presents a detail analysis using data in Xiong'an new area from September 2020 to September 2021. Then, this paper proposes a time-series algorithm to identify ridesharing behavior of demand responsive (DR) bus and to recommend indicators on operation efficiency considering capacity, turnover, and time. Moreover, the definition of indicators and calculation of case study are carried out. Results show that the utilization rate of vehicle seat for DR bus was increased by 1.6-2.5 times, and the turnover efficiency was increased by about 2 times compared to private cars and taxi. In general, this paper quantitatively describes the improvement of operation efficiency brought by bus sharing, which shows that this kind of shared mobility has the attributes of public transport in a certain sense. Also, this paper shows that the above indicators are quantifiable and comparable, which is a useful supplement to the existing evaluation index system., Author(s): Heng Du (corresponding author) [1]; Yu Wang [1]; Lin Shi [1]; Yuxuan Xing [2] 1. Introduction Recently, variety of travel services such as online ride-hailing, carpooling, car-sharing rental, and [...]

The extensive snow cover across the Tibetan Plateau (TP) regions has a major influence on the climate and water supply for over one billion downstream inhabitants. However, an adequate evaluation of snow cover fraction (SCF) variability over the TP simulated by global multiple reanalysis datasets has yet to be undertaken. In this study, we examined eight global reanalysis SCF datasets using the Snow Property Inversion from Remote Sensing (SPIReS) product spanning the period 2001–2020. The results reveal that the HMASR generated the best SCF simulations because of its outstanding spatial and temporal accuracy. The GLDAS and CFSR demonstrated acceptable SCF accuracy with respect to spatial variability, but struggled to reproduce the annual trend. Pronounced SCF overestimations were found when using the ERA5, ERA5L, and JRA55, but SCF was underestimated by MERRA2, and CRAL generated poor spatial pattern. Overall biases were related to the combined effect of precipitation forcing, temperature forcing, snow data assimilation, and SCF parameterization methods, with the dominant factor changing across datasets. In ERA5 and ERA5L, temperature and snowfall bias exhibited significant correlations with SCF bias over most TP areas, therefore having a greater impact on the accuracy of SCF in terms of spatial variability and temporal evolution. On the other hand, the impact of snow assimilation was possibly more pronounced in MERRA2 and CRAL. Although parameterization methods can improve SCF simulation accuracy, their influence was weaker than those of other factors, except for JRA55. To further improve the accuracy of SCF simulation, an ensemble average method was developed. The ensemble based on HMASR and GLDAS generated the most accurate SCF spatial distribution, whereas the ensemble containing ERA5L, CFSR, CRAL, GLDAS, ERA5, and MERRA2 proved optimal for capturing the annual trend. [ABSTRACT FROM AUTHOR]

The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of >2100, >600, and >630 km2 in the Tien Shan, Kunlun, and Qilian Mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust depositions is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian Mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust event. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. Through this study, we gain a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote-sensing to monitor large-scale snow darkening. [ABSTRACT FROM AUTHOR]