Weakening warming on spring freeze-thaw cycle caused greening Earth's third pole.

The Tibetan Plateau, recognized as Earth's third pole and among the most responsive regions to climate shifts, profoundly influences regional and even global hydrological processes. Here, we discerned a significant weakening in the influence of temperature on the initiation of surface freeze-thaw cycle (the Start of Thawing, SOT), which can be ascribed to a multitude of climatic variables, with radiation emerging as the most pivotal factor. Additionally, we showed that the diminishing impact of warming on SOT yields amplified soil moisture within the root zone. This, in turn, fosters a greening third pole with increased leaf area index and solar-induced chlorophyll fluorescence. We further showed that current Earth system models failed to reproduce the linkage between weakened sensitivity and productivity under various shared socioeconomic pathways. Our findings highlight the dynamic shifts characterizing the influence of climate warming on spring freeze-thaw process and underscore the profound ecological implications of these changes in the context of future climate scenarios.

Spring wood phenology responds more strongly to chilling temperatures than bud phenology in European conifers.

A comparative assessment of bud and wood phenology could aid a better understanding of tree growth dynamics. However, the reason for asynchronism or synchronism in leaf and cambial phenology remains unclear. To test the assumption that the temporal relationship between the budburst date and the onset date of wood formation is due to their common or different responses to environmental factors, we constructed a wood phenology dataset from previous literature, and compared it with an existing bud phenology dataset in Europe. We selected three common conifers (Larix decidua Mill., Picea abies (L.) H. Karst. and Pinus sylvestris L.) in both datasets and analyzed 909 records of the onset of wood formation at 47 sites and 238,720 records of budburst date at 3051 sites. We quantified chilling accumulation (CA) and forcing requirement (FR) of budburst and onset of wood formation based on common measures of CA and FR. We then constructed negative exponential CA-FR curves for bud and wood phenology separately. The results showed that the median, variance and probability distribution of CA-FR curves varied significantly between bud and wood phenology for three conifers. The different FR under the same chilling condition caused asynchronous bud and wood phenology. Furthermore, the CA-FR curves manifested that wood phenology was more sensitive to chilling than bud phenology. Thus, the FR of the onset of wood formation increases more than that of budburst under the same warming scenarios, explaining the stronger earlier trends in the budburst date than the onset date of woody formation simulated by the process-based model. Our work not only provides a possible explanation for asynchronous bud and wood phenology from the perspective of organ-specific responses to chilling and forcing, but also develops a phenological model for predicting both bud and wood phenology with acceptable uncertainties.

Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event.

Attribution of compound events informs preparedness for emerging hazards with disproportionate impacts. However, the task remains challenging because space-time interactions among extremes and uncertain dynamic changes are not satisfactorily addressed in the well-established attribution framework. For attributing the 2020 record-breaking spatially compounding flood-heat event in China, we conduct a storyline attribution analysis by designing simulation experiments via a weather forecast model, quantifying component-based attributable changes, and comparing with historical flow analogs. We quantify that given the large-scale circulation, anthropogenic influence to date has exacerbated the extreme Mei-yu rainfall in the mid-lower reaches of the Yangtze River during June-July 2020 by ~6.5% and warmed the co-occurring seasonal extreme heat in South China by ~1°C. Our projections show a further intensification of the compound event by the end of this century, with moderate emissions making the rainfall totals ~14% larger and the season ~2.1°C warmer in South China than the 2020 status.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

A 2.5° × 2.5° gridded drought/flood grades dataset for eastern China during the last millennium.

Hydroclimate reconstruction for the last millennium is essential to understand the differences in hydroclimate extremes and their causes under cold/warm conditions. In this study, the first gridded drought/flood grades (D/F grades) dataset in eastern China (EC) during the last millennium was generated. This D/F grades dataset mainly consisted of two components. The first section was created by interpolating drought/flood grades from 1500 to 2000 using the angular distance weight method. Sampling error estimates were employed to assess the effects of the interpolated dataset. The second section for the D/F grades dataset during 960-1500 was generated by constructing best subset regression models using selected tree-ring chronologies in the United States through atmospheric teleconnection. The validation parameters of the calibration equations were also derived, including the adjusted R2, predicted R2, RE, and CE. This dataset provides critical support for investigating the characteristics and causes of hydroclimate extremes in EC at various spatiotemporal scales, as well as the relationship with climate modes, such as El Niño-Southern Oscillation, Pacific Decadal Oscillation, and East Asia Summer Monsoon.

Unintended consequences of combating desertification in China.

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region.

Fine particulate matter (PM2.5) trends from land surface changes and air pollution policies in China during 1980-2020.

High levels of fine particulate matter (PM2.5) pose a severe air pollution challenge in China. Both land use changes and anthropogenic emissions can affect PM2.5 concentrations. Only a few studies have addressed the long-term impact of land surface changes on PM2.5 in China. We conducted a comprehensive analysis of PM2.5 trends over China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) during 1980-2020. The monthly mean PM2.5 concentrations of MERRA-2 were evaluated across mainland China against independent surface measurements from 2013 to 2020, showing a good agreement. For the trend analysis, China was subdivided into six regions based on land use and ambient aerosols types. Our results indicate an overall significant PM2.5 increase over China during 1980-2020 with major changes in-between. Notwithstanding continued urbanization and associated anthropogenic activities, the PM2.5 reversed to a downward trend around 2007 over most regions except for the part of China that is most affected by desert dust. Statistical analysis suggests that PM2.5 trends during 1980-2010 were associated with urban expansion and deforestation over eastern and southern China. The trend reversal around 2007 is mainly attributed to Chinese air pollution control measures. A multiple linear regression analysis reveals that PM2.5 variability is linked to soil moisture and vegetation. Our results suggest that land use and land cover changes as well as pollution controls strongly influenced PM2.5 trends and that drought conditions affect PM2.5 particularly over desert and forest regions of China. This work contributes to a better understanding of the changes in PM2.5 over China.

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia.

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

Winter warming offsets one half of the spring warming effects on leaf unfolding.

Winter temperature-related chilling and spring temperature-related forcing are two major environmental cues shaping the leaf-out date of temperate species. To what degree insufficient chilling caused by winter warming would slow phenological responses to spring warming remains unclear. Using 27,071 time series of leaf-out dates for 16 tree species in Europe, we constructed a phenological model based on the linear or exponential function between the chilling accumulation (CA) and forcing requirements (FR) of leaf-out. We further used the phenological model to quantify the relative contributions of chilling and forcing on past and future spring phenological change. The results showed that the delaying effect of decreased chilling on the leaf-out date was prevalent in natural conditions, as more than 99% of time series exhibited a negative relationship between CA and FR. The reduction in chilling linked to winter warming from 1951 to 2014 could offset about one half of the spring phenological advance caused by the increase in forcing. In future warming scenarios, if the same model is used and a linear, stable correlation between CA and FR is assumed, declining chilling will continuously offset the advance of leaf-out to a similar degree. Our study stresses the importance of assessing the antagonistic effects of winter and spring warming on leaf-out phenology.

Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products.

As a routine agricultural practice, irrigation is fundamental to protect crops from water scarcity and ensure food security in China. However, consistent and reliable maps about the spatial distribution and extent of irrigated croplands are still unavailable, impeding water resource management and agricultural planning. Here, we produced annual 500-m irrigated cropland maps across China for 2000-2019, using a two-step strategy that integrated statistics, remote sensing, and existing irrigation products into a hybrid irrigation dataset. First, we generated intermediate irrigation maps (MIrAD-GI) by fusing the MODIS-derived greenness index and statistical data. Second, we collected all existing available irrigation maps over China and integrated them with MIrAD-GI into an improved series of annual irrigation maps, using constrained statistics and a synergy mapping method. The resultant maps had moderate overall accuracies (0.732~0.819) based on nationwide reference ground samples and outperformed existing irrigation products by inter-comparison. As the first of this kind in China, the annual maps delineated the spatiotemporal pattern of irrigated croplands and could contribute to sustainable water use and agricultural development.

Modeling the effect of adaptation to future climate change on spring phenological trend of European beech (Fagus sylvatica L.).

Temperate trees could cope with climate change through phenotypic plasticity of phenological key events or adaptation in situ via selection on genetic variation. However, the relative contribution of local adaptation and phenotypic plasticity to phenological change is unclear for many ecologically important tree species. Here, we analyzed the leaf-out data of European beech (Fagus sylvatica L.) from 50 provenances planted in 7 trial sites. We first constructed a function between chilling accumulation (CA) and photoperiod-associated heat requirement (PHR) of leaf-out date for each provenance and quantified the relationship between parameters of the CA-PHR function and climatic variables at provenance origins by using the random forest model. Furthermore, we used the provenance-specific CA-PHR function to simulate future leaf-out dates under two climate change scenarios (RCP 4.5 and 8.5) and two assumptions (no adaptation and adaptation). The results showed that both CA, provenance, and their interactions affected the PHR of leaf-out. The provenances from southeastern Europe exhibited a stronger response of PHR to CA and thus flushed earlier than northwestern provenances. The parameters of the CA-PHR function were connected with climatic variables (e.g., mean diurnal temperature range, temperature seasonality) at the originating sites of each provenance. If only considering the phenotypic plasticity, the leaf-out date of European beech in 2070-2099 will advance by 6.8 and 9.0 days on average relative to 1951-2020 under RCP 4.5 and RCP 8.5, respectively. However, if F. sylvatica adapts to future climate change by adopting the current strategy, the advance of the leaf-out date will weaken by 1.4 and 3.4 days under RCP 4.5 and RCP 8.5, respectively. Our results suggest that the European beech could slow down its spring phenological advances and reduce its spring frost risk if it adopts the current strategy to adapt to future climate change.

Stronger Spring Phenological Advance in Future Warming Scenarios for Temperate Species With a Lower Chilling Sensitivity.

Spring warming could induce earlier leaf-out or flowering of temperate plant species, and decreased chilling in winter has a delaying effect on spring phenology. However, the relative contribution of the decreased chilling and increased forcing on spring phenological change is unclear. Here, we analyzed the experimental data for 14 temperate woody species in Beijing, China and quantified the forcing requirements (FR) of spring phenology and chilling sensitivity (the ratio of the FR at the low chilling condition to the FR at the high chilling condition) for each species. Furthermore, using species-specific functions between the amount of chilling and FR, we established a phenological model to simulate the annual onset dates of spring events during the past 69 years (1952-2020) and in the future (2021-2099) under RCP 4.5 and RCP 8.5 climate scenarios. We also developed a novel method to quantitatively split the predicted phenological change into the effects caused by changes in forcing and those caused by changes in chilling. The results show that the FR of spring events decreased with the increase in the amount of chilling, and this relationship could be described as an exponential decay function. The basic FR (the FR at the high chilling condition) and chilling sensitivity varied greatly among species. In the 1952-2020 period, the advancing effect of increased forcing was stronger than the effect of chilling, leading to earlier spring events with a mean trend of -1.96 days/decade. In future climate scenarios, the spring phenology of temperate species would continue to advance but will be limited by the decreased chilling. The species with lower chilling sensitivities showed stronger trends than those with high chilling sensitivities. Our results suggested that the delaying effect of declining chilling could only slow down the spring phenological advance to a certain extent in the future.

Low temperature and short daylength interact to affect the leaf senescence of two temperate tree species.

Temperature and photoperiod are two major environmental cues shaping the leaf senescence of temperate tree species. However, how the control of leaf senescence is split between photoperiod and temperature is unknown for many ecologically important species. Here, we conducted a growth chamber experiment to test the effects of temperature (6, 9, 18 and 21°C) and photoperiod (8 and 16 h daylength) on leaf senescence of two temperate tree species (Quercus mongolica Fisch. and Larix principis-rupprechtii Mayr.) distributed in montane forest of China. The results showed that low temperature (LT) alone could induce leaf senescence of both species under long daylength (LD) conditions, but the leaf senescence of L. principis-rupprechtii was more sensitive to the decrease in temperature than that of Q. mongolica under the LD condition. Short daylength (SD) alone could only induce the leaf senescence of L. principis-rupprechtii, suggesting that the photoperiod sensitivity varies between species. SD could accelerate the LT-induced senescence, but the effect of SD reduced with the decrease in temperature. Based on these findings, we developed a new autumn phenology model by incorporating interspecific differences in the photoperiod sensitivity of leaf senescence. Compared with the three existing process-based autumn phenology models, the new model was more robust in simulating the experimental data. When employing these models to available long-term phenological data, our new model also performed best in reproducing the observed leaf senescence date of two closely related species (Quercus robur L. and Larix decidua Mill.). These results enhance our understanding of how LT and SD control leaf senescence. The prediction of the climate change impacts on forest carbon uptake could be improved by incorporating this new autumn phenological model into the terrestrial biosphere models.

Modelling armed conflict risk under climate change with machine learning and time-series data.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Assessing environmental thresholds in relation to plant structure and nutritional value for improved maize calendar ensuring food security.

The environment has been continuously changed, and it's a bitter truth that we can't minimize anthropogenic activities to mitigate harmful impacts on the environment. The changing environment is a great threat to food security by affecting crop yields. However, there is no comprehensive study to assess the environmental impact on the nutritional quality of the crops. In this study, we have investigated the nutritional profile and yield of maize crops around the globe and synchronized the findings with physiological reasoning. The study enlightens the time-scale activities of maize plant enzymes and describes their response to changing environments. The study also explained time-scale-based changes in the physiological conditions of maize crops against environmental dynamics around the globe. It also detected the impact of climate change on the deterioration of the nutritional quality of maize. The current study reports the activities of three different enzyme classes. It was noted that the photosynthesis-related enzyme activities were boosted after a sudden increase in carbon dioxide concentration. However, the drought years (2005-2010) decreased photosynthesis and increased oxidative enzyme activities. Overall, the glycemic index of the maize crop has been increased during the last four decades. However, the crop production threshold levels have been raised more quickly. The nutritional index values are alarming and have frequently been recorded under the threshold levels in recent years. The study paves a path for maize toward nutritional contents richness, ensuring food security and nutritional security in the future.

Climate-associated major food crops production change under multi-scenario in China.

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011-2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36-91.18% and 62.96-64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12-68.93% and 40.44-41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09-53.94% and 35.86-37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Change of extreme snow events shaped the roof of traditional Chinese architecture in the past millennium.

As a symbol of civilization and culture, architecture was originally developed for sheltering people from unpleasant weather or other environmental conditions. Therefore, architecture is expected to be sensitive to climate change, particularly to changes in the occurrence of extreme weather events. However, although meteorological factors are widely considered in modern architecture design, it remains unclear whether and how ancient people adapted to climate change from the perspective of architecture design, particularly on a millennium time scale. Here, we show periodic change and a positive trend in roof slope of traditional buildings in the northern part of central and eastern China and demonstrate climate change adaptation in traditional Chinese architecture, driven by fluctuations in extreme snowfall events over the past thousand years. This study provides an excellent example showing how humans have long been aware of the impact of climate change on daily life and learned to adapt to it.

Assessing the adaptability of maize phenology to climate change: The role of anthropogenic-management practices.

Phenology has been regarded as an essential bio-indicator of climate change widely. Quantifying the crop phenological changes caused by climate change and anthropogenic-management practices can help formulate effective climate change adaptation strategies. In this study, the effects of climate change and anthropogenic-management practices on maize phenology (spring, summer, and intercropping maize) in China were distinguished based on historical meteorological and phenological data (1981-2010) of 114 stations using the first-order difference regression method. Our results show: (1) The vegetative growing period of spring and intercropping maize was extended, whereas that of summer maize was shortened. The reproductive growing periods of spring, summer, and intercropping maize were extended. (2) Isolated impacts of climate change shortened the vegetative growing period of spring maize, summer maize, and intercropping maize by 0.19, 1.06, and 3.12 d decade-1, respectively, while the reproductive growing period was extended by 0.19, 0.74, and 3.47 d decade-1, respectively. (3) The contribution of temperature to maize phenology was greater in the northwest inland maize zone and north spring maize zone than in other regions, whereas the contribution of sunshine hours was higher in Huang-Huai Plain intercropping maize zone and the southwest mountain hills maize zone. (4) The effects of anthropogenic-management practices on maize phenological stages such as sowing, emergence, and maturity were generally greater than that of climate change, which has delayed the phenological stages of summer and intercropping maize and extended the growing period of spring maize. The focus should be paid to the emergence, jointing, and milky stages to increase the water use efficiency in the northwest inland maize zone. The findings provide a scientific basis for improving the adaptability of agricultural systems in climate change.

Widespread decline in winds delayed autumn foliar senescence over high latitudes.

The high northern latitudes (>50°) experienced a pronounced surface stilling (i.e., decline in winds) with climate change. As a drying factor, the influences of changes in winds on the date of autumn foliar senescence (DFS) remain largely unknown and are potentially important as a mechanism explaining the interannual variability of autumn phenology. Using 183,448 phenological observations at 2,405 sites, long-term site-scale water vapor and carbon dioxide flux measurements, and 34 y of satellite greenness data, here we show that the decline in winds is significantly associated with extended DFS and could have a relative importance comparable with temperature and precipitation effects in contributing to the DFS trends. We further demonstrate that decline in winds reduces evapotranspiration, which results in less soil water losses and consequently more favorable growth conditions in late autumn. In addition, declining winds also lead to less leaf abscission damage which could delay leaf senescence and to a decreased cooling effect and therefore less frost damage. Our results are potentially useful for carbon flux modeling because an improved algorithm based on these findings projected overall widespread earlier DFS than currently expected by the end of this century, contributing potentially to a positive feedback to climate.