Future Temperature Extremes Will Be More Harmful: A New Critical Factor for Improved Forecasts

Key periods of peatland development and environmental changes in the middle taiga zone of Western Siberia during the Holocene

The response of peatlands to climate change can be highly variable. Through understanding past changes we can better predict the response of peatlands to future climate change. We use a multi-proxy approach to reconstruct the surface wetness and carbon accumulation of the Mukhrino mire (Western Siberia), describing the development of the mire since peat formation in the early Holocene, around 9360 cal. year BP. The mire started as a rich fen which initiated after paludification of a spruce forest (probably in response to a wetter climate), while the Mukhrino mire progressed to ombrotrophic bog conditions (8760 cal. year BP). This transition coincided with the intensive development of mires in Western Siberia and was associated with active carbon accumulation (31 g m-2 year-1). The ecosystem underwent a change to a tree-covered state around 5860 cal. year BP, likely in response to warming and possible droughts and this accompanied low carbon accumulation (12 g m2 year-1). If the future climate will be warmer and wetter, then regional mires are likely to remain a carbon sink, alternatively, a reversion to the wooded state with reduced carbon sink strength is possible.

Atmospheric pollution in the ten most populated US cities. Evidence of persistence

The degree of persistence in daily PM₂₅ and O₃ in the ten most populated US cities, namely New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas and San Jose is examined in this work. We employ a methodology based on fractional integration, using the order of integration as a measure of the degree of persistence. Using data for the time period from January 1, 2019 to December 31, 2020, our results indicate that fractional integration and long memory features are both present in all the examined cases, with the integration order of the series being constrained in the (0, 1) interval. Based on this, the estimation of the coefficients for the time trend produces results which are substantially different from those obtained under the I (0) assumption.

Spatial and Temporal Variation Characteristics of Heatwaves in Recent Decades over China

Global warming and rapid socioeconomic development increased the risk of regional and global disasters. Particularly in China, annual heatwaves (HWs) caused many fatalities and substantial property damage, with an increasing trend. Therefore, it is of great scientific value and practical importance to analyze the spatiotemporal changes of HW in China for the sustainable development of regional socioeconomic and disaster risk management. In this study, based on gridded maximum temperature product and specific humidity dataset, an HW evaluation algorithm, considering the impact of humidity on the human body and the characteristics of HW in China, was employed to generate daily HW state at light, moderate, and severe levels for the period 1979–2018. Consequently, the regional differences at three HW levels were revealed, and the changing trend of HW onset, termination, and duration in each subregion was analyzed. The results show that in the three levels, the frequency and duration of HW in China had a significant increasing trend, generally characterized by the advancement of HW onset and the postponement of HW termination. The HW influence at light, moderate and severe levels decreased gradually, with the light level occurring the earliest and terminating the latest. Among the seven subregions, the largest HW frequency happened to be mainly in XJ (Xinjiang), SC (Southern China), and NC (Northern China), while the variations of HW onset and termination had noticeable regional differences at the three levels. The findings presented in this study can provide the essential scientific and technological support for national and regional disaster prevention mitigation and adaptation to extreme climate 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.

Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO₂ emissions. It represents the CO₂ emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO₂ flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO₂ flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO₂ flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.

A New Climate Nowcasting Tool Based on Paleoclimatic Data

Atmospheric pollutants and environmental indicators are often used to reconstruct historic atmospheric pollution from peat, as it accumulates over time by decomposing plant material, thus recording a history of air pollution. In the present study, three key parameters related to the peat bogs’ surface wetness dynamics in European Russia during the Holocene were investigated using modern statistical analysis. These parameters are: (i) the water table depth (WTD) in relation to the surface, which is reconstructed based on the community structure of the subfossil testate amoeba assemblages; (ii) the peat humification estimated as absorption of alkaline extract that directly reflects moisture at which the peat was formed; (iii) the Climate Moisture Index (CMI) and the Aridity Index derived from pollen-based reconstructions of the mean annual temperature and precipitation and classifying moisture conditions as the ratio between available annual precipitation and potential land surface evapotranspiration. All these parameters provide useful information about the paleoclimate (atmospheric moisture component) dynamics. High values of WTD and peat humification appear to comply with Gutenberg–Richter law. It is noteworthy that this law also seems to reproduce the high values of the modeled climate moisture and aridity indices. The validity of this new result is checked by replacing “conventional time” with “natural time”. On this basis, a new nowcasting tool is developed to more accurately estimate the average waiting time for the extreme values of these climate parameters. This will help to understand climate variability better to address emerging development needs and priorities by implementing empirical studies of the interactions between climatic effects, mitigation, adaptation, and sustainable growth.