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The simulated sea surface temperature anomaly (SSTA) over the tropical Pacific during El Niño-Southern Oscillation (ENSO) is investigated in three representative coupled models: CESM1-CAM5, FGOALS-s2, and FGOALS-g2. It is found that there is a significant westward shift bias in reproducing the zonal distribution (ZD) of the ENSO-related SSTA in CESM1-CAM5 and FGOALS-s2, whereas the SSTA-ZD simulated by FGOALS-g2 is relatively realistic. Through examining the SSTA-ZD during both warm and cold phases of ENSO separately, the authors reveal that the SSTA-ZD simulation bias during the ENSO cycle mainly lies in the bias during the warm phase. It is noted that both the simulated zonal wind stress anomaly ( ) and shortwave heat flux (SW) anomaly exhibit westward shift biases in CESM1-CAM5 and FGOALS-s2, while the counterparts in FGOALS-g2 are relatively reasonable. The westward shift biases in representing and the SW anomaly (SWA) are attributed to the westward-shifted precipitation anomaly (PrA). It is suggested that the mean SST cold bias over the cold tongue region is the key factor behind the westward-shift bias in simulating the El Niño-related PrA, which leads to the westward-shifted and SWA. Collectively, the aforementioned anomaly fields, including the dynamic part ( ) and thermodynamic part (SWA), contribute to the westward-shift bias in simulating the El Niño-related SSTA. This study provides clues for understanding the ZD simulation biases of ENSO-related fields; however, further in-depth investigation with more model simulations, especially the incoming CMIP6 simulations, is still needed to fully understand the ENSO SSTA-ZD simulation bias in coupled models.

Variations in the El Niño/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts 1 . Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic 2 . Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations 3 and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms. A statistical forecast model using a deep-learning approach produces useful forecasts of El Niño/Southern Oscillation events with lead times of up to one and a half years.

Deep learning (DL) has achieved notable success in El Niño‐Southern Oscillation (ENSO) forecasts. Most DL‐based models focused on forecasting ENSO indices while the zonal distribution of sea surface temperature anomalies (SSTA) over the equatorial Pacific was overlooked. To provide accurate predictions for the SSTA zonal pattern, this study developed a model through leveraging the merits of the cosine distance in constructing the convolutional neural network. This model can skillfully predict the SSTA zonal pattern over the equatorial Pacific 1 year in advance, remarkably outperforming current dynamical models. Moreover, the physical interpretation of the model prediction reveals that the sources for ENSO predictability at different lead times are distinct. For the 10‐month‐lead predictions, the precursors in the north Pacific, south Pacific and tropical Atlantic play critical roles in determining the model behaviors; while for the 16‐month‐lead predictions, the initial signals in the tropical Pacific associated with the discharge‐recharge cycle are essential. Plain Language Summary The El Niño‐Southern Oscillation (ENSO) is the most prominent climate phenomenon in the Earth system. It significantly affects the worldwide weather and climate via teleconnections. Numerous studies have reported that the ENSO teleconnection and its impacts largely depend on the zonal distribution of SSTA over the equatorial Pacific. Thus, the ENSO forecast with the specific SSTA zonal pattern is important for anticipating the severity of ENSO‐related disasters and mitigating the potential socio‐economic impacts. However, current dynamical models have difficulties in accurately predicting the SSTA zonal pattern, while most of deep learning models only provide predictions of ENSO indices. Hence, we developed a deep learning model based on the convolutional neural network which can effectively predict the SSTA zonal pattern 1 year in advance. Moreover, we investigate the interpretability of this model by analyzing activation maps. The results suggest that crucial factors captured by this model at different lead times are physically reasonable, which verify the credibility of this model. Key Points We develop a deep learning model that can skillfully predict the explicit sea surface temperature anomalies (SSTA) zonal pattern over the equatorial Pacific 1 yr ahead Physical interpretation shows that the source of 10‐month‐lead prediction stems from the Pacific Meridional Mode, South Pacific quadrupole, and tropical Atlantic SSTA The main source of 16‐month‐lead forecast comes from discharge/recharge cycles, implying distinct prediction sources at different lead times

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, spatially resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. Integrating spatial information from MERFISH with the more complete transcriptome produced by single-nucleus RNA sequencing (snRNA-seq), also reveals zonally enriched receptor-ligand interactions. Finally, MERFISH and snRNA-seq analysis of fibrotic liver samples identify two hepatocyte populations that expand with injury and do not have clear zonal distributions. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling that drives disease which, in turn, could provide new avenues for intervention and further study. Spatial transcriptomics was combined with single-nucleus RNA sequencing to annotate healthy and fibrotic human livers, improving spatial resolution of hepatocytes and receptor-ligand interactions and identifying cell populations that expand with injury.

This study investigates the fundamental causes of differences in the Madden–Julian oscillation (MJO) eastward propagation among models that participated in a recentmodel intercomparison project. These models are categorized into good and poor groups characterized by prominent eastward propagation and nonpropagation, respectively. Column-integrated moist static energy (MSE) budgets are diagnosed for the good and the poor models. It is found that a zonal asymmetry in the MSE tendency, characteristic of eastward MJO propagation, occurs in the good group, whereas such an asymmetry does not exist in the poor group. The difference arises mainly from anomalous vertical and horizontal MSE advection. The former is attributed to the zonal asymmetry of upper-midtropospheric vertical velocity anomalies acting on background MSE vertical gradient; the latter is mainly attributed to the asymmetric zonal distribution of low-tropospheric meridional wind anomalies advecting background MSE and moisture fields. Based on the diagnosis above, a new mechanism for MJO eastward propagation that emphasizes the second-baroclinic-mode vertical velocity is proposed. A set of atmospheric general circulation model experiments with prescribed diabatic heating profiles was conducted to investigate the causes of different anomalous circulations between the good and the poor models. The numerical experiments reveal that the presence of a stratiform heating at the rear of MJO convection is responsible for the zonal asymmetry of vertical velocity anomaly and is important to strengthening lower-tropospheric poleward flows to the east of MJO convection. Thus, a key to improving the poor models is to correctly reproduce the stratiform heating. The roles of Rossby and Kelvin wave components in MJO propagation are particularly discussed.

Large deformation has always been a focus and difficult issue in the construction of deep-buried tunnels in squeezing rock. Previous studies mainly focused on the large deformation of medium and small span railway/highway tunnels in soft ground. However, there are limited researches on the large deformation control methods for large-span (three-lane) highway tunnels constructed in unfavorable geological environment. Based on the Lianchengshan Tunnel of the Baoji-Hanzhong expressway in Shaanxi Province, China, this paper studied the deformation behaviors and mechanical mechanisms of a large-span tunnel excavated in chlorite schist formation with single primary lining method and double primary lining method by in-situ test and numerical simulation. The achieved results indicate that the double primary lining method is much more effective than that of the single primary lining method in restraining the deformation of surrounding rock, and the maximum vertical displacement and horizontal convergence are reduced by 67% and 66%, respectively. The support method of double HK200b-type steel sets combined with large-diameter foot reinforcement bolt (FRB) and deep invert could effectively control the large deformation of the case tunnel, which effectively avoided the supporting structure failure, repeated clearance invasion and multiple reshaping work caused by the single primary lining method and conformed to the energy-saving construction concept of “no clearance interfering, no support reshaping” of tunnels in squeezing ground. Simulation analysis of surrounding rock deformation, supporting structure stress and plastic zone distribution was performed to evaluate the support effect of the two deformation-controlled methods. Finally, the deformation and stress characteristic curves of rock-support of the two deformation-controlled methods were established, which revealed the supporting mechanism of double primary linings for large-span tunnels in chlorite schist. The research results can provide a theoretical basis and practical reference for the large-deformation control of similar large-span tunnels in squeezing rock.

The ecological environment in Loess Plateau of Northern Shaanxi is fragile, so the soil pollution caused by the exploitation of coal resources cannot be ignored. With Shigetai Coal Mine in Loess Plateau of Northern Shaanxi as the object of study for field survey and sampling, the content of heavy metals in soil is analyzed, the environmental pollution in the research area is evaluated by the single factor pollution index method, comprehensive pollution index method and potential ecological risk index method, and the spatial distribution characteristics of heavy metals are discussed by the geostatistics method. According to the study results, the average contents of heavy metals Hg, Cd, Pb and Cr are 2.03, 1.36, 1.11 and 1.23 times of the soil background values in Shaanxi Province respectively and the average contents of other heavy metals are lower than the soil background values in Shaanxi Province; Hg and Cd show moderate variation while As, Pb, Cr, Zn, Ni and Cu show strong variation; the skewness coefficients and kurtosis coefficient of Cd, As and Cu in the soil within the research area are relatively high, and these elements are accumulated in large amounts. Single factor pollution index (Pi) and potential ecological risk index (E) indicate that heavy metal Hg is the main pollution factor and mainly distributed in the east and north of the research area. The comprehensive index of potential ecological risk (RI) of the research area is 1336.49, showing an extremely high ecological risk, and the distribution characteristics of potential ecological risk are consistent with that of potential ecological risk index (E) of Hg. The results of ecological risk warning show that Hg is in a slight warning status, while Cd, Pb and Cr are in a warning status. The areas with high ecological risk warning values are mainly distributed in the east and north, and the whole research area shows relatively obvious zonal distribution law. The soil is disturbed greatly during the coal mining, so the ecological governance of the mine area shall adapt to the local natural conditions and regional environmental characteristics and follow the principle of “adjusting governance measures based on specific local conditions and classifications”. An environmentally sustainable governance manner shall be adopted to realize the protection of the ecological environment and high-quality development of coal resources.

Chronic liver damage leads to pathological accumulation of ECM proteins (liver fibrosis). Comprehensive characterization of the human ECM molecular composition is essential for gaining insights into the mechanisms of liver disease. To date, studies of ECM remodeling in human liver diseases have been hampered by the unavailability of purified ECM. Here, we developed a decellularization method to purify ECM scaffolds from human liver tissues. Histological and electron microscopy analyses demonstrated that the ECM scaffolds, devoid of plasma and cellular components, preserved the three-dimensional ECM structure and zonal distribution of ECM components. This method has been then applied on 57 liver biopsies of HCV-infected patients at different stages of liver fibrosis according to METAVIR classification. Label-free nLC-MS/MS proteomics and computation biology were performed to analyze the ECM molecular composition in liver fibrosis progression, thus unveiling protein expression signatures specific for the HCV-related liver fibrotic stages. In particular, the ECM molecular composition of liver fibrosis was found to involve dynamic changes in matrix stiffness, flexibility and density related to the dysregulation of predominant collagen, elastic fibers and minor components with both structural and signaling properties. This study contributes to the understanding of the molecular bases underlying ECM remodeling in liver fibrosis and suggests new molecular targets for fibrolytic strategies.

Injecting sulfur dioxide into the stratosphere with the intent to create an artificial reflective aerosol layer is one of the most studied options for solar radiation management. Previous modelling studies have shown that stratospheric sulfur injections have the potential to compensate for the greenhouse-gas-induced warming at the global scale. However, there is significant diversity in the modelled radiative forcing from stratospheric aerosols depending on the model and on which strategy is used to inject sulfur into the stratosphere. Until now, it has not been clear how the evolution of the aerosols and their resulting radiative forcing depends on the aerosol microphysical scheme used – that is, if aerosols are represented by a modal or sectional distribution. Here, we have studied different spatio-temporal injection strategies with different injection magnitudes using the aerosol–climate model ECHAM-HAMMOZ with two aerosol microphysical modules: the sectional module SALSA (Sectional Aerosol module for Large Scale Applications) and the modal module M7. We found significant differences in the model responses depending on the aerosol microphysical module used. In a case where SO2 was injected continuously in the equatorial stratosphere, simulations with SALSA produced an 88 %–154 % higher all-sky net radiative forcing than simulations with M7 for injection rates from 1 to 100 Tg (S) yr−1. These large differences are identified to be caused by two main factors. First, the competition between nucleation and condensation: while injected sulfur tends to produce new particles at the expense of gaseous sulfuric acid condensing on pre-existing particles in the SALSA module, most of the gaseous sulfuric acid partitions to particles via condensation at the expense of new particle formation in the M7 module. Thus, the effective radii of stratospheric aerosols were 10 %–52 % larger in M7 than in SALSA, depending on the injection rate and strategy. Second, the treatment of the modal size distribution in M7 limits the growth of the accumulation mode which results in a local minimum in the aerosol number size distribution between the accumulation and coarse modes. This local minimum is in the size range where the scattering of solar radiation is most efficient. We also found that different spatial-temporal injection strategies have a significant impact on the magnitude and zonal distribution of radiative forcing. Based on simulations with various injection rates using SALSA, the most efficient studied injection strategy produced a 33 %–42 % radiative forcing compared with the least efficient strategy, whereas simulations with M7 showed an even larger difference of 48 %–116 %. Differences in zonal mean radiative forcing were even larger than that. We also show that a consequent stratospheric heating and its impact on the quasi-biennial oscillation depend on both the injection strategy and the aerosol microphysical model. Overall, these results highlight the crucial impact of aerosol microphysics on the physical properties of stratospheric aerosol which, in turn, causes significant uncertainties in estimating the climate impacts of stratospheric sulfur injections.

Mesoscale Convective Systems (MCSs) play a critical role in tropical rainfall patterns and circulations. To reduce persistent biases and improve understanding of the climate system, international groups have called for unprecedented investment in global convection–permitting (CP) climate models. It is essential such models accurately represent MCSs, and in particular environmental interactions such as dynamical control by wind shear. We show that in representative current generation CP simulations, MCS updraft entrainment decreases with shear, leading to a realistic increase of extreme rainfall. We find the control of environmental shear extends to mean storm rainfall and anvil heights. The simulation of these effects depends strongly on model physics in both CP and parameterized models. We show that in West Africa, MCS shear response influences the zonal distribution of storm diabatic heating, modifying upscale impacts of convection. Our results demonstrate key tests for focused process–based assessment of CP model fidelity.