A New Climate Nowcasting Tool Based on Paleoclimatic Data

Scaling Behavior of Peat Properties during the Holocene: A Case Study from Central European Russia

A better understanding of past climate change is vital to our ability to predict possible future environmental dynamics. This study attempts to investigate the dynamic features of the temporal variability of peat humification, water table depth and air temperature by analyzing palaeoecological data from the Valdai Uplands region (Central European Russia). The regression analysis revealed the presence of a periodicity of about 6000 years in the reconstructed peat humification timeseries. Nonlinear analysis showed that humification time variability, water table depth and air temperature exhibit persistent long-range correlations of 1/f type. This indicates that a fluctuation in these variables in the past is very likely to be followed by a similar one in the future, but is magnified by 1/f power-law. In addition, it dictates that humification, water table depth and temperature are key parameters of a system that implies the existence of a special structure, such as self-organized criticality, operating close to a minimum stability configuration, and achieves it without any fine adjustment by external forcing. These conclusions point to new avenues for modeling future ecosystem disturbances and, in particular, for predicting relevant extreme events.

Increased susceptibility to temperature variation for non-accidental emergency ambulance dispatches in Shenzhen, China

Most studies focused on the temporal trend of mortality risk associated with temperature exposure. The relative role of heat, cold, and temperature variation (TV) on morbidity and its temporal trends are explored insufficiently. This study aims to investigate the temporal trends of emergency ambulance dispatch (EAD) risk and the attributable burden of heat, cold, and hourly temperature variation (HTV). We collected time-series data of daily EAD and ambient temperature in Shenzhen from 2010 to 2017. HTV was calculated as the standard deviation of the hourly temperatures between 2 consecutive days. Quasi-Poisson generalized additive models (GAM) with a time-varying distributed lag nonlinear model (DLNM) were applied to examine temporal trends of the HTV-, heat-, and cold-EAD association. The temporal variation of the attributable fraction (AF%) and attributable number (AN) for different temperature exposures was also calculated. The largest RR was observed in extreme cold [1.30 (95% CI: 1.18, 1.43)] and moderate cold [1.25 (95% CI: 1.17, 1.34)]. Significant increasing trends in HTV-related effects and burden were observed, especially for the extreme HTV effects (P for interaction < 0.05). Decreasing trends were observed in the heat-related effect and burden, though it showed no significance (P for interaction = 0.46). There was no clear change pattern of cold-related effects and burdens. Overall, the three temperature exposure caused 13.7% of EAD, of which 4.1%, 4.3%, and 5.3% were attributed to HTV, heat, and cold, respectively. All the temperature indexes in this study, especially the cold effect, are responsible for the increased risk of EAD. People have become more susceptible to HTV over the recent decade. However, there is no clear evidence to support the temporal change of the population's susceptibility to heat and cold. Thus, in addition to heat and cold, the emergency ambulance service department should pay more attention to HTV under climate change.

A new model for the spread of COVID-19 and the improvement of safety

COVID-19 has been spreading rapidly around the world since December 2019. The main goal of this study is to develop a more effective method for diagnosing and predicting the COVID-19 spread and to evaluate the effectiveness of control measures to reduce and stop the virus spread. To this end, the COVID-19 Decision-Making System (CDMS) was developed to study disease transmission. CDMS divides the population into groups as susceptible, infected, cured and dead. The trends of the people's number in these groups have deterministic and stochastic components. The deterministic components are described by a differential equations system with parameters determined by the data reported. The stochastic components are represented as an indicator of instability that characterizes the tendency of COVID-19 spread. The simulation experiments have shown a good agreement between the CDMS estimates and the data reported in Russia and Greece. The analysis performed showed that the newly-introduced instability indicator may be the precursor to the pandemic dynamics. In this context, our results showed three potential candidates for a second wave of COVID-19 disease: USA, Russia and Brazil. Although the proportion of infected individuals in countries with high temperatures is lower than in European countries and Russia, temperature and humidity are slowly affecting the effects of the pandemic. Finally, the results presented may contribute to the urgent need to reduce the risks associated with the second wave of the COVID-19, to improve public health intervention and safety measures to be taken by various countries.