Crossover behavior in dynamics of frozen ferrofluids

The imaginary part of susceptibility, chi , in frozen ferrofluids was measured as functions of temperature, frequency, and concentration of magnetic particles. In a very dilute region, where magnetic interaction is negligible, the relaxation time is mainly determined by Neel\u27s relaxation in isolated particles and the frequency-dependent peak temperature in chi (T), T-p, obeys the Arrhenius law. In the moderate concentration range, T-p has a Vogel-Fulcher (VF) relaxation with the measuring frequency. However, for concentration larger than a certain value, deviation from VF law occurs. Simulations show that the VF relation could be attributed to Ising spin-glass-like random and frustrated interaction between magnetic moments of particles. We suggest that particle configuration before freezing is critical to determine the distribution of pair exchange parameters. For high concentration, there are more ferromagnetic than antiferromagnetic bonds. The deviation from VF law at high concentration could be due to short range correlation among spins

Thermodynamic model of electric-field-induced pattern formation in binary dielectric fluids

An electric-field-induced phase transition and pattern formation in a binary dielectric fluid layer are studied using a coarse-grained free-energy functional. The electrostatic part of the free energy is a nonlinear functional of the dielectric function, which depends in turn on the local colloidal concentration. We determine the phase coexistence curve and find that beyond a critical electric field the system phase separates. Accompanying the phase separation are patterns similar to those observed in a spinodal decomposition of an ordinary binary fluid. The temporal evolution of the phase separating patterns are discussed both analytically and numerically by integrating a Cahn-Hilliard type of equation

Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic

Coronavirus disease 2019 (COVID-19) has been spreading rapidly and is still threatening human health currently. A series of measures for restraining epidemic spreading has been adopted throughout the world, which seriously impacted the gross domestic product (GDP) globally. However, details of the changes in the GDP and its spatial heterogeneity characteristics on a fine scale worldwide during the pandemic are still uncertain. We designed a novel scheme to simulate a 0.1° × 0.1° resolution grid global GDP map during the COVID-19 pandemic. Simulated nighttime-light remotely sensed data (SNTL) was forecasted via a GM(1, 1) model under the assumption that there was no COVID-19 epidemic in 2020. We constructed a geographically weighted regression (GWR) model to determine the quantitative relationship between the variation of nighttime light (ΔNTL) and the variation of GDP (ΔGDP). The scheme can detect and explain the spatial heterogeneity of ΔGDP at the grid scale. It is found that a series of policies played an obvious role in affecting GDP. This work demonstrated that the global GDP, except for in a few countries, represented a remarkably decreasing trend, whereas the ΔGDP exhibited significant differences

Detecting the causal effect of soil moisture on precipitation using convergent cross mapping

As a vital land surface parameter, soil moisture influences climate through its impact on water and energy cycles. However, the effect of soil moisture on precipitation has been strongly debated. In this study, a new causal detection method, convergent cross mapping (CCM), was applied to explore the causality between soil moisture and precipitation over low- and mid-latitude regions in the Northern Hemisphere. CCM method generally identified a strong effect of soil moisture on precipitation. Specifically, the optimal effect of soil moisture on precipitation occurred with a lag of one month and clearly decreased after four months, suggesting that soil moisture has potentials to improve the accuracy of precipitation forecast at a sub-seasonal scale. In addition, as climate (i.e., aridity index) changed from dry to wet, the effect of soil moisture on precipitation first increased and then decreased with peaks in semi-arid and semi-humid areas. These findings statistically support the hypothesis that soil moisture impacts precipitation and also provide a reference for the design of climate prediction systems

Human and natural impacts on the water resources in the Syr Darya River Basin, Central Asia

Water resources are increasingly under stress in Central Asia because downstream countries are highly dependent on upstream countries. Water is essential for irrigation and is becoming scarcer due to climate change and human activities. Based on 20 hydrological stations, this study firstly analyzed the annual and seasonal spatial-temporal changes of the river discharges, precipitation, and temperature in the Syr Darya River Basin and then the possible relationships between these factors were detected. Finally, the potential reasons for the river discharge variations have been discussed. The results show that the river discharges in the upper stream of the basin had significantly risen from 1930 to 2006, mainly due to the increase in temperature (approximately 0.3 degrees C per decade), which accelerated the melting of glaciers, while it decreased in the middle and lower regions due to the rising irrigation. In the middle of the basin, the expansion of the construction land (128.83 km(2)/year) and agricultural land (66.68 km(2)/year) from 1992 to 2015 has significantly augmented the water consumption. The operations of reservoirs and irrigation canals significantly intercepted the river discharge from the upper streams, causing a sharp decline in the river discharges in the middle and lower reaches of the Syr Darya River in 1973. The outcomes obtained from this study allowed us to understand the changes in the river discharges and provided essential information for effective water resource management in the Syr Darya River Basin

Description and attribution analysis of the 2017 spring anomalous high temperature causing floods in Kazakhstan

It is speculated that floods in many areas of the world have become more severe with global warming. This study describes the 2017 spring floods in Kazakhstan, which, with about six people dead or missing, prompted the government to call for more than 7,000 people to leave their homes. Then, based on the Climatic Research Unit (CRU), the NCEP/NCAR Reanalysis 1, and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, the seasonal trends of temperature were calculated using the linear least-squares regression and the Mann–Kendall trend test. The correlation between the surface air temperature and atmospheric circulation was explored, and the attributable risk of the 2017 spring floods was evaluated using the conventional fraction of the attributable risk (FAR) method. The results indicate that the north plains of Kazakhstan had a higher (March–April) mean temperature anomaly compared to the south plains, up to 3°C, relative to the 1901-2017 average temperature. This was the primary cause of flooding in Kazakhstan. March and April were the other months with a higher increasing trend in temperature from 1901 to 2017 compared with other months. In addition, a positive anomaly of the geopotential height and air temperature for the March–April 2017 period (based on the reference period 1961-1990) was the reason for a warmer abnormal temperature in the northwest region of Kazakhstan. Finally, the FAR value was approximately equal to 1, which supported the claim of a strong anthropogenic influence on the risk of the 2017 March–April floods in Kazakhstan. The results presented provide essential information for a comprehensive understanding of the 2017 spring floods in Kazakhstan and will help government officials identify flooding situations and mitigate damage in future