General circulation models for soil moisture simulation in global land areas: a complex network perspective

Under global warming, dry and hot events have been increasing in recent decades and are projected to increase in the future across global land areas. The impacts of compound dry and hot events may lead to increased stress to the natural and human systems than separate dry or hot events. Thus, quantitative assessments of global land areas affected by these compound events are needed to understand their risks. This study focuses on the variation in global land and cropland areas affected by compound dry and hot events for both historical and future periods using observations from Climatic Research Unit (CRU) and simulations from Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Based on historical observations and simulations, a substantial increase in the spatial extent of these compound events was detected, especially since the 1980s. Climate model projections under the Representative Concentration Pathway (RCP) 8.5 scenario reveal that both global land and cropland areas affected by compound dry and hot events will increase to approximately 1.7–1.8 times by the end of the 21st century. Based on different thresholds of compound events, the spatial extent of global land areas during the June–July–August (December–January–February) season will increase by 12.38–17.20% (7.83–11.19%) in 2050–2099 relative to that in 1950–1999, and the spatial extent of global cropland areas will increase by 14.69–19.63% (9.60–14.48%). The increase in areas affected by compound dry and hot events may lead to more losses in different sectors in the future and more efforts are needed to cope with their potential impacts.

We use monthly precipitation simulated by a high‐resolution global climate model (the MIROC4h) to examine the effects of spatial and temporal coverage on the estimation of mean, trend, and variability of precipitation for large land regions and the global land area. We consider spatial and temporal coverage typical of publicly available precipitation data sets of in situ station observations. We find that the spatial coverage of these data sets is not sufficient for the estimation of total precipitation for the global and hemispheric land areas and for some large regions considered. Estimates of global and hemispheric total land precipitation tend to be biased to higher values due to undersampling in low precipitation regions. The existing station coverage may nevertheless provide reasonable estimates for the magnitude of trend and variability in global to regional land area mean precipitation. However, the incomplete spatial coverage of the observational records results in larger sampling errors in trend estimates, making it harder to detect statistically significant trends. Publicly available gridded precipitation data that are based on larger collection of stations (all of which are not publicly available) may provide a better alternative for the time being.

The frequency and intensity of extreme weather and climate events may change in response to shifts in the mean and variability of climate, which pose high risks to societies and natural ecosystems. Gridded near-surface temperature, precipitation, and the number of wet days from the Climatic Research Unit dataset were analyzed for two 30 year periods to explore changes in the mean and variability of temperature and precipitation over global land areas in the recent period (1991–2020) compared to the reference period (1951–1980). Global land areas are characterized by warmer and slightly wetter conditions in the recent period, while the variability of temperature and precipitation has remained nearly unchanged. Changes in the mean and variability of both temperature and precipitation are also analyzed over tropical, subtropical, and midlatitude land areas. The annual mean temperature over all these three latitudinal regions has increased in the recent period compared to the reference period, with the highest increase in subtropical and midlatitude land areas (0.7 ∘C), followed by tropical land areas (0.5 ∘C), while temperature variability has remained nearly unchanged. The annual precipitation has decreased over tropical, subtropical, and midlatitude land areas in the recent period compared to the reference period. Precipitation variability has not changed considerably over subtropical land areas. However, it has substantially increased over tropical land areas, which indicates a higher risk of droughts and periods of excess water in the recent period. In contrast, precipitation variability has decreased over midlatitude land areas, indicating narrower swings between wet and dry conditions, which decrease the risk of droughts and periods of excess water in the recent period.

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

China has become the world’s largest market for the production and sales of new energy vehicles. In the Internet era, online review data is becoming more and more important, and it is a great challenge for new energy vehicle manufacturers and consumers to quickly obtain and find out the effective information in the review data. In view of the above understanding, this study uses the Bert-wwm-ext model structure, data mining, and deep learning to study the new energy vehicle selection, and also analyzes the positioning of domestic and foreign new energy vehicle brands and their brand development from the perspective of complex networks. The research results found that: 1) Consumers pay more and more attention to the quality of new energy vehicles. 2) The comparative analysis of BEV and PHEV reveals that consumers’ evaluation of both types of vehicles is roughly comparable, but the difference in satisfaction with the endurance of both types of vehicles is very obvious. 3) Most of the brands of new energy vehicles are concentrated in the price range of RMB80,000 to RMB350,000, and within this range, consumers’ evaluation is positively correlated with the price of the vehicle. Among the new energy vehicle brands over RMB350,000, consumer evaluation does not rise with the price of the vehicle. 4) The head effect of Chinese brands is significant, Foreign brands have formed strong brands with high brand premiums.

Remarkable increases in compound dry and hot events (CDHEs) have been observed in different regions in recent decades. However, the anthropogenic influence on the long-term changes in CDHEs at the global scale has been largely unquantified. In this study, we provide evidence that anthropogenic forcings have contributed to the increased CDHEs over global land areas. We compare the spatial and temporal changes in CDHEs based on climate model simulations from Coupled Model Intercomparison Project Phase 6 and observations from different datasets. The results show observed occurrences of CDHEs have increased over most regions across global land areas during 1956–2010 relative to 1901–1955. In addition, we find a temporal increase in observed occurrences of CDHEs averaged over global land areas and different continents (except Antarctica) for the period 1901–2010 (with a larger increase during 1951–2010). The spatial and temporal changes in historical all-forcing simulations (with both anthropogenic and natural components) are overall consistent with observations, while those in historical natural-forcing simulations diverge substantially from observations, heightening the key role of anthropogenic forcings in increased CDHEs. Furthermore, we use the probability ratio (PR) to quantify the contribution of anthropogenic forcings to the likelihood of CDHEs since the mid-20th century (1951–2010). We find anthropogenic influences have increased the risk of CDHEs in large regions across the globe except for parts of Eurasia and North America. Overall, our study highlights the important role of anthropogenic influences in increased CDHEs from a global perspective. The mitigation of climate change is thus paramount to reduce the risk of CDHEs.

Quantitative contribution of ENSO to precipitation-temperature dependence and associated compound dry and hot events

Global warming has been shown to affect weather and climate extremes, such as droughts, floods, windstorms, cold waves, and heat waves. A number of studies have focused on the variability of different characteristics of these extremes, including their frequency, spatial extent, and severity. Recently, the study of compound extremes, defined by the co-occurrence of multiple events with extreme impacts, has attracted much attention. The compound dry and hot extreme is one type of compound extreme and may lead to detrimental impacts on the society and ecosystem. Most previous studies have focused on changes in the frequency or spatial extent of compound dry and hot extremes, while assessments of changes in the severity of compound extremes are lacking. This study evaluated changes in the severity of compound dry and hot extremes at the global scale, based on the Standardized Dry and Hot Index (SDHI). A significant increase in the severity of compound dry and hot extremes (or decrease of the SDHI value) during the warm season was found in western US, northern South America, western Europe, Africa, western Asia, southeastern Asia, southern India, northeastern China and eastern Australia. Moreover, a significant temporal increase in the average severity of the hottest month over global land areas was also observed. Results from this study highlight the increased severity of compound dry and hot extremes over global land areas and call for improved efforts on assessing the impact of compound extremes under global warming.

A complex-network perspective on Alexander’s wholeness

The wholeness, conceived and developed by Christopher Alexander, is what exists to some degree or other in space and matter, and can be described by precise mathematical language. However, it remains somehow mysterious and elusive, and therefore hard to grasp. This paper develops a complex network perspective on the wholeness to better understand the nature of order or beauty for sustainable design. I bring together a set of complexity-science subjects such as complex networks, fractal geometry, and in particular underlying scaling hierarchy derived by head/tail breaks—a classification scheme and a visualization tool for data with a heavy-tailed distribution, in order to make Alexander’s profound thoughts more accessible to design practitioners and complexity-science researchers. Through several case studies (some of which Alexander studied), I demonstrate that the complex-network perspective helps reduce the mystery of wholeness and brings new insights to Alexander’s thoughts on the concept of wholeness or objective beauty that exists in fine and deep structure. The complex-network perspective enables us to see things in their wholeness, and to better understand how the kind of structural beauty emerges from local actions guided by the 15 fundamental properties, and by differentiation and adaptation processes. The wholeness goes beyond current complex network theory towards design or creation of living structures.

The visibility graph (VG) and horizontal visibility graph (HVG) have been recently introduced as a mapping between the time series and the complex networks. Here, the relativistic nucleus–nucleus interaction data, $$^{24}$$ Mg-Ag/Br at 4.5 A GeV/c are analyzed and used to characterizing the multifractal properties by extending VG, HVG and the sandbox algorithm (SB). In this work, the HVG–SB techniques have been applied to confirm the scale-freeness and the presence of fractal properties in the process of multiplicity fluctuation. We try to find deterministic information by means of precise determination of topological parameter from a complex network perspective. We have analyzed the degree distributions which are associated with event-wise pseudorapidity distributions, typical of any multifractality measurement, different patterns of symmetry scaling, scale-freeness, correlation and clustering among the produced particles. We try to find deterministic information by means of precise determination of topological parameter from a complex network perspective. It has been also observed that the multifractal variables were derived by SB algorithm for the experimental data sets to some extent distinct from the respective Monte Carlo simulated results.

The evolution of Tehran subway networks has been investigated from a complex network perspective in order to analyze their structure, topology and functions. Tehran Metro networks are characterized by a large number of interconnected stations, which makes them highly dynamic and nonlinear systems. From a complex network perspective, The stations are the network’s nodes, and the lines connecting them are its edges. Metro networks display scale-free and small-world features, according to previous investigations, with preferential attachment forming the basis for their formation. Additionally, the evolution of Tehran metro networks is driven by transportation demand, economic activities, geography and urban planning. This study shows that the better network has been obtained during the evolution as the degree, clustering coefficient, and the shortest path length have been improved in the third phase but network robustness is not so high against to the attacks.

Likelihoods of compound dry-hot-windy events are projected to increase under global warming

Of rising concern is the consecutive occurrence of contrasting precipitation extremes such as droughts followed by severe floods, which tips a precarious balance with lasting impacts on human and natural systems. However, it is not clear how the risk of such precipitation reversals may change over time at the global scale. Using precipitation data from the Community Earth System Model Version 1 (CESM1) Large Ensemble Simulations (LENS), we examined changes in the likelihood and spatial distribution of low‐intensity dry‐to‐wet fluctuations and more severe shifts from meteorological droughts to pluvials over global land areas via a conditional statistical framework. Our analysis of CESM1‐LENS leads to the conclusion that the worst‐case climate change scenario, that is, RCP8.5 can increase the percent of global land areas susceptible to both low‐intensity and more severe precipitation reversals by the end of the twenty‐first century, while inducing disproportionate impacts at regional scales depending on the severity of precipitation reversals. Particularly, we find 13.6% of global land areas may be depressed by interannual dry‐to‐wet events with a transition probability of greater than 0.5 in the twenty‐first century, quintupling the figure in the past century. The increase in the susceptible global land areas further indicates that new local hotspots with low‐frequency drought‐pluvial seesaws are more likely to emerge under the effects of enhancing anthropogenic greenhouse gases. Collectively, the study is expected to provide useful information for policy makers and infrastructure operators to prioritize actions that affect water resource objectives of flood control, drought mitigation, and water quality protection.

Projected climate regime shift under future global warming from multi-model, multi-scenario CMIP5 simulations