Simulation Skills of the SST-Forced Global Climate Variability of the NCEP–MRF9 and the Scripps–MPI ECHAM3 Models

The global responses of two atmospheric general circulation models (AGCM), the National Centers for Environmental Prediction–Medium Range Forecast (NCEP–MRF9) and the University of Hamburg climate model–3 (ECHAM), to simultaneous global SST forcing are examined on a 3-month timescale. Rotated principal components analysis of the model and observations is also used to identify and compare their leading modes of coherent variability. The scope of the present analyses is largely descriptive and does not attempt to explain the differences in model behavior in terms of their formulations. The authors’ main focus is to quantify the simulation skill of the two comprehensive AGCMs on seasonal timescales and compare it to skill obtained using empirical prediction models. Both models are found to exhibit realistic responses to El Niño–Southern Oscillation (ENSO)-related forcing, with the ECHAM response slightly more accurate in the spatial phasing and structure of the atmospheric anomalies. The ECHAM model exhibits realistic atmospheric responses to tropical Pacific SST forcing as well as patterns associated with extratropical internal atmospheric dynamics [e.g., North Atlantic oscillation (NAO) and a high latitude north–south dipole in the Pacific]. It shows a slightly higher signal-to-noise ratio than that found in the real world, while the NCEP model’s signal-to-noise ratio is approximately equal to that in nature. The NCEP model responds with more zonally symmetric atmospheric patterns than observed, although this does not prevent it from forming realistic responses to ENSO over the Pacific–North American region. The NCEP model’s NAO variability is only about half as strong as that observed. In terms of simulation skill with respect to observations, the ECHAM model generally tends to outperform the NCEP model for global 500-hPa geopotential height and surface climate. A decomposition of the observed and model data into rotated principal components indicates that both models reproduce the ENSO-related anomalies in circulation and surface climate of the real atmosphere quite well. The ECHAM model, which handles ENSO variability and impacts slightly better than the NCEP model, shows a larger increment of capability in reproducing other global climate processes. Two linear statistical benchmarks, which are used as skill control measures, sometimes outperform the NCEP model but are more comparable, on average, to the skill of the ECHAM model. Thus as noted in other recent studies, the dynamical models and the statistical models have roughly the same simulation skill and would be expected to have similar forecast skill if the models used forecasted SSTs as their boundary conditions. To first order, the linear component of the relationships appears to be modeled well by the two dynamical models. It is undetermined whether instances of better performance of the dynamical models than the statistical benchmarks are partly attributable to the models’ effective exploitation of nonlinearities in the relationships between tropical SST and global climate. One reason for this inconclusiveness is that evidence for nonlinearities in the present analyses is not compelling. Hence, the question of whether dynamical models have untapped potential to consistently outperform statistical models on the seasonal timescale remains open and may require close examination of each physical formulation in the dynamical models

Changes in the Spread of the Variability of the Seasonal Mean Atmospheric States Associated with ENSO

For a fixed sea surface temperature (SST) forcing, the variability of the observed seasonal mean atmospheric states in the extratropical latitudes can be characterized in terms of probability distribution functions (PDFs). Predictability of the seasonal mean anomalies related to interannual variations in the SSTs, therefore, entails understanding the influence of SST forcing on various moments of the probability distribution that characterize the variability of the seasonal means. Such an understanding for changes in the first moment of the PDF for the seasonal means with SSTs is well documented. In this paper the analysis is extended to include also the impact of SST forcing on the second moment of the PDFs. The analysis is primarily based on ensemble atmospheric general circulation model (AGCM) simulations forced with observed SSTs for the period 1950–94. To establish the robustness of the results and to ensure that they are not unduly affected by biases in a particular AGCM, the analysis is based on simulations from four different AGCMs. The analysis of AGCM simulations indicates that over the Pacific–North American region, the impact of interannual variations in SSTs on the spread of the seasonal mean atmospheric states (i.e., the second moment of the PDFs) may be small. This is in contrast to their well-defined impact on the first moment of the PDF for the seasonal mean atmospheric state that is manifested as an anomalous wave train over this region. For seasonal predictions, the results imply that the dominant contribution to seasonal predictability comes from the impact of SSTs on the first moment of the PDF, with the impact of SSTs on the second moment of the PDFs playing a secondary role

An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation. Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

The recent US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability" was unequivocal in recommending the need for the development of a North American Multi-Model Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) then any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, an operational European system and there are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test Bed (CTB) NMME workshops (February 18, and April 8, 2011) a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data is readily available (e.g., http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/) and in graphical format from CPC (http://origin.cpc.ncep.noaa.gov/products/people/wd51yf/NMME/index.html). Moreover, the NMME forecast are already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, presents an overview of the multi-model forecast quality, and the complementary skill associated with individual models

Review of regulating Zn2+ solvation structures in aqueous zinc-ion batteries

Aqueous zinc-ion batteries, due to their high power density, intrinsic safety, low cost, and environmental benign, have attracted tremendous attentions recently. However, their application is severely plagued by the inferior energy density and short cycling life, which was mainly ascribed to zinc dendrites, and interfacial side reactions, narrow potential window induced by water decomposition, all of which are highly related with the Zn ^2+ solvation structures in the aqueous electrolytes. Therefore, in this review, we comprehensively summarized the recent development of strategies of regulating Zn ^2+ solvation structures, specially, the effect of zinc salts, nonaqueous co-solvents, and functional additives on the Zn ^2+ solvation structures and the corresponding electrochemical performance of aqueous zinc-ion batteries. Moreover, future perspectives focused on the challenges and possible solutions for design and commercialization of aqueous electrolytes with unique solvation structures are provided

Dynamic mechanical behavior and cracking mechanism of cross-jointed granite containing a hole

The cavity disasters induced by dynamic disturbances are hazards to the cavity safety and may cause substantial damage to the entire subsurface project. The dynamic mechanical responses of the cavern are crucially important in rock engineering design and stability analysis. In this article, the dynamic mechanical and cracking behaviors of the jointed surrounding rock of the cavern were investigated experimentally by testing cross-jointed granite containing a hole using a split Hopkinson pressure bar. The results indicate that the dynamic peak strength and elastic modulus decrease as β increases from 15° to 45° and increase gradually as β varies from 45° to 75°, and those two parameters show an overall increasing trend with increasing impact pressure. The reflected and absorbed energy ratios first increase and then decrease with increasing β, while the transmitted energy ratio first decreases and then increases. Under the same impact pressure, the absorbed energy ratio is far higher than the reflected and transmitted energy ratios. With increasing impact pressure, the transmitted and absorbed energy ratios increase overall, while the reflected energy ratio generally decreases. Additionally, the cracking process and final failure modes of the samples show a significant joint structure effect and strain rate effect. Nevertheless, the tensile cracks along the impact direction play a dominant role in the dynamic fracture, and tensile failure is the main failure type, with shear failure occurring locally. These findings provide the theoretical basis for preventing dynamic disasters in the cavity and are of great significance to rock engineering safety

Synthesis of Mg–K-biochar bimetallic catalyst and its evaluation of glucose isomerization

Abstract Highly efficient isomerization of glucose to fructose is essential for valorizing cellulose fraction of biomass to value-added chemicals. This work  provided an innovative method for preparing Mg-biochar and Mg–K-biochar catalysts by impregnating either MgCl2 alone or in combination with different K compounds (Ding et al. in Bioresour Technol 341:125835, 2021, https://doi.org/10.1016/j.biortech.2021.125835 and KHCO3) on cellulose-derived biochar, followed by hydrothermal carbonization and pyrolysis. Single active substance MgO existing in the 10Mg–C could give better catalytic effect on glucose isomerization than the synergy of MgO and KCl crystalline material present in 10Mg–KCl–C. But the catalytic effect of 10Mg–C was decreased when the basic site of MgO was overloaded. Compared to other carbon-based metal catalysts, 10Mg–KHCO3–C with 10 wt% MgCl2 loading had  excellent catalytic performance, which gave  a higher fructose yield (36.7%) and selectivity (74.54%), and catalyzed excellent glucose conversion (53.99%) at 100 °C in 30 min. Scanning electron microscope–energy dispersive spectrometer and X-Ray diffraction revealed that the distribution of Mg2+ and K+ in 10Mg–KHCO3–C  was uniform and the catalytic active substances (MgO, KCl and K2CO3) were more than 10Mg–C (only MgO). The synergy effects of MgO and K2CO3 active sites enhanced  the pH of reaction system and  induced H2O ionization to form considerable OH− ions, thus easily realizing a deprotonation of glucose and effectively catalyzing the isomerization of glucose. In this study, we developed a highly efficient Mg–K-biochar bimetallic catalyst for glucose isomerization and provided  an efficient method for cellulose valorization. Graphical Abstrac

Dynamic Compressive Mechanical Property Characteristics and Fractal Dimension Applications of Coal-Bearing Mudstone at Real-Time Temperatures

Coal-bearing rocks are inevitably exposed to high temperatures and impacts (rapid dynamic load action) during deep-earth resource extraction, necessitating the study of their mechanical properties under such conditions. This paper reports on dynamic compression tests conducted on coal-bearing mudstone specimens at real-time temperatures (the temperature of the rock remains constant throughout the impact process) ranging from 25 °C to 400 °C using a temperature Hopkinson (T-SHPB) test apparatus developed in-house. The objective is to analyze the relationship between mechanical properties and the fractal dimension of fractured fragments and to explore the mechanical response of coal-bearing mudstone specimens to the combined effects of temperature and impact using macroscopic fracture characteristics. The study found that the peak stress and dynamic elastic modulus initially increased and then decreased with increasing temperature, increasing in the 25–150 °C range and monotonically decreasing in the 150–400 °C range. Based on the distribution coefficients and fractal dimensions of the fractured fragments, it was found that the degree of damage of coal-bearing mudstone shows a trend of an initial decrease and then an increase with increasing temperature. In the temperature range of 25–150 °C, the expansion of clay minerals within the mudstone filled the voids between the skeletal particles, resulting in densification and decreased damage. In the temperature range of 150–400 °C, thermal stresses increased the internal fractures and reduced the overall strength of the mudstone, resulting in increased damage. Negative correlations between fractal dimensions, the modulus of elasticity, and peak stress could be used to predict rock properties in engineering

Piezoresistive Sensor Containing Lamellar MXene-Plant Fiber Sponge Obtained with Aqueous MXene Ink

Sustainable biomass materials are promising for low-cost wearable piezoresistive pressure sensors, but these devices are still produced with time-consuming manufacturing processes and normally display low sensitivity and poor mechanical stability at low-pressure regimes. Here, an aqueous MXene ink obtained by simply ball-milling is developed as a conductive modifier to fabricate the multiresponsive bidirectional bending actuator and compressible MXene-plant fiber sponge (MX-PFS) for durable and wearable pressure sensors. The MX-PFS is fabricated by physically foaming MXene ink and plant fibers. It possesses a lamellar porous structure composed of one-dimensional (1D) MXene-coated plant fibers and two-dimensional (2D) MXene nanosheets, which significantly improves the compression capacity and elasticity. Consequently, the encapsulated piezoresistive sensor (PRS) exhibits large compressible strain (60%), excellent mechanical durability (10 000 cycles), low detection limit (20 Pa), high sensitivity (435.06 kPa–1), and rapid response time (40 ms) for practical wearable applications

Targeting macrophage M1 polarization suppression through PCAF inhibition alleviates autoimmune arthritis via synergistic NF-κB and H3K9Ac blockade

Abstract Sustained inflammatory invasion leads to joint damage and progressive disability in several autoimmune rheumatic diseases. In recent decades, targeting M1 macrophage polarization has been suggested as a promising therapeutic strategy for autoimmune arthritis. P300/CBP-associated factor (PCAF) is a histone acetyltransferase (HAT) that exhibits a strong positive relationship with the proinflammatory microenvironment. However, whether PCAF mediates M1 macrophage polarization remains poorly studied, and whether targeting PCAF can protect against autoimmune arthritis in vivo remains unclear. Commonly used drugs can cause serious side effects in patients because of their extensive and nonspecific distribution in the human body. One strategy for overcoming this challenge is to develop drug nanocarriers that target the drug to desirable regions and reduce the fraction of drug that reaches undesirable targets. In this study, we demonstrated that PCAF inhibition could effectively inhibit M1 polarization and alleviate arthritis in mice with collagen-induced arthritis (CIA) via synergistic NF-κB and H3K9Ac blockade. We further designed dextran sulfate (DS)-based nanoparticles (DSNPs) carrying garcinol (a PCAF inhibitor) to specifically target M1 macrophages in inflamed joints of the CIA mouse model via SR-A–SR-A ligand interactions. Compared to free garcinol, garcinol-loaded DSNPs selectively targeted M1 macrophages in inflamed joints and significantly improved therapeutic efficacy in vivo. In summary, our study indicates that targeted PCAF inhibition with nanoparticles might be a promising strategy for treating autoimmune arthritis via M1 macrophage polarization inhibition