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Abstract In the post-COVID-19 pandemic era, the world faces a choice between trade protection and cooperation. However, current literature provides very little information on the benefit or loss of trade friction or cooperation on global economy and climate mitigation. This study applied the Global Trade Analysis Project model to assess the impacts of trade friction on global economic recovery and climate change. The results indicated that international trade friction can both delay global economic recovery and affect CO2 emission reduction. The shocks of consumption reduction and production suspension have a higher marginal effect in developing and emerging economies, whereas trade friction has a higher effect on developed countries. Trade friction has more negative economic effects for developed countries, but developing countries cannot reduce CO2 emissions proportionally with the decrease in trade and related production. In the post-pandemic era, if the global trade barrier increases, the world may face a co-occurring economic decline and an increase in or low abatement of carbon emissions.

Abstract Flash droughts pose large threats to crop yields and ecosystem services due to their sudden onset and rapid intensification, arousing wide public concern in a warming climate. Their long-term characteristics of change, underlying mechanisms, and especially potential impacts on agriculture, forests, and populations at a global scale, however, remain largely unknown. We used in situ observations, two observation-based global reanalysis data sets, and 22 Earth system models to determine that flash droughts are shifting toward more frequent, accelerated-onset, and longer duration. These changes increased the exposure of agricultural areas, forested areas, and populations to flash droughts by 20.3%, 17.1%, and 30.0%, respectively, during 2001–2020 compared to 1981–2000, with a disproportionate increase in integrated risks across the Amazon Basin and eastern and southern Asia. The increase in concurrent hot and dry climatic conditions driven by warming has been mostly responsible for enabling and intensifying flash droughts over large regions. State-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) models, however, failed to identify the acceleration of the onset time of flash droughts and widely underestimated the occurrence of flash droughts that are driven only by precipitation deficits or by heat waves, probably because they misrepresent the dependence between precipitation and temperature and underestimate the sensitivity of soil moisture to temperature and precipitation at short timescales (e.g., 5 days). These syntheses comprehensively advance our understanding of the characteristics and impacts of flash droughts but also highlight that the CMIP6 models need to be validated to represent the correct covariability between climatic variables at short timescales to provide more reliable projections of flash droughts.

The auto-generation of urban roads can greatly improve efficiency and productivity in urban planning and designing. However, it has also raised concerns amongst researchers over the past decade. In this paper, we present an image-based urban road network generation framework using conditional diffusion models. We first trained a diffusion model capable of generating road images with similar characteristics to the ground truth using four context factors. Then, we used the trained model as the generator to synthesize road images conditioned in a geospatial context. Finally, we converted the generated road images into road networks with several post-processes. The experiments conducted in five cities of the United States showed that our model can generate reasonable road networks, maintaining the layouts and styles of real examples. Moreover, our model has the ability to show the obstructive effect of geographic barriers on urban roads. By comparing models with different context factors as input, we find that the model that considers all four factors generally performs the best. The most important factor in guiding the shape of road networks is intersections, implying that the development of urban roads is not only restricted by the natural environment but is more strongly influenced by human design.

Abstract 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 R 2 , predicted R 2 , 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.

This paper shows that desertification combating practices decline incomes of farmers and herders, and China needs to adapt its ecological programmes to address the impacts of climate change and create positive synergies to combat desertification.

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.

High levels of sulfur dioxide (SO2) due to human activities pose a serious air pollution issue in China, especially in urban agglomerations. However, limited research has investigated the impact of anthropogenic emissions on higher SO2 concentrations in urban regions compared to rural areas in China. Here, we analyzed the trends in SO2 concentrations from 1980 to 2021 in China using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) dataset. SO2 column concentrations from the Copernicus Atmosphere Monitoring Service (CAMS) and the Ozone Monitoring Instrument (OMI) during the years 2007–2021 were also examined for validation and comparison purposes. Eight representative areas, including four urban regions (Pearl River Delta [PRD], Beijing-Tianjin-Hebei [BTH], Yangtze River Delta [YRD], and Sichuan Basin [SCB]) and four rural regions (Northeast Region [NER], Mongolian Region [MR], West Region [WR], and Tibetan Plateau Region [TR]) were selected for the analysis. Overall, a significant but fluctuating increase in SO2 concentrations over China was observed during 1980–2021. During 1980–1997 and 2000–2010, there was an increase in SO2 concentration, while during 1997–2000 and 2010–2021, a decreasing trend was observed. The average increase in SO2 concentration was approximately 16 times higher in urban regions than in the rural background. We also found that SO2 dynamics were highly associated with expansion of urban areas, population density, and gross domestic product. Nonetheless, since 2007, SO2 concentrations have exhibited a downward trend, which is mainly attributed to the air pollution policies implemented by the Chinese government. Our findings highlight the need for further studies on the impact of SO2 on regional climate change in China.

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.

Abstract Given that armed conflict has been seriously impeding sustainable development, reducing the frequency and intensity of armed conflicts has become an explicit goal and a common theme of the 2030 Sustainable Development Goals. Determining the factors shaping armed conflict risks in different regions could support formulating region-specific strategies to prevent armed conflicts. A machine learning approach was applied to reveal the drivers of, and especially the impact of climatic conditions on, armed conflict in Sub-Saharan Africa, the Middle East, and South Asia and characterizes their changes over time. The analyses show a rising impact of climatic conditions on armed conflict risk over the past decades, although the influences vary regionally. The overall percentage increases in the contribution of climatic conditions to conflict risks over the last 30 years in Sub-Saharan Africa, the Middle East, and South Asia are 4.25, 4.76, and 10.65 percentage points, respectively. Furthermore, it is found that the Climatic–Social–Geographical (“C–S–G”) patterns that characterize armed conflict risks vary across the three studied regions, while each regional pattern remains relatively stable over time. These findings indicate that when devising defenses against conflicts, it is required to adapt to specific situations in each region to more effectively mitigate the risk of armed conflict and pursue Sustainability Development Goals.

Measurement(s) irrigation area and distribution Technology Type(s) statistics and satellite remote sensing Factor Type(s) agricultural irrigation Sample Characteristic - Organism agricultural irrigation Sample Characteristic - Environment agricultural field Sample Characteristic - Location China

Using machine learning, the authors reveal that stable background conditions explain most variation in armed conflict risk worldwide. Positive temperature deviations and precipitation extremes also increase the risk of conflict onset and incidence.

Agricultural irrigation is an important vehicle for increasing crop yield, but large-scale irrigation has posed great challenges to global and regional water availability and climate change via altering land–atmosphere interactions. The knowledge of irrigation distribution is essential to understand regional water cycles and guide agricultural management decision-making, but such information is scarce in China. We developed a remote sensing-dominated framework to map irrigated croplands in China at 500 m resolution using a synergetic training sample generating method, machine learning classifier, and a cloud computing platform (Google Earth Engine, GEE). To overcome the challenges of lacking nationwide training samples, we first produced two provisional irrigation maps by fusing statistics and MODIS-derived annual peak greenness indices. The two provisional irrigation maps were then spatially filtered with an existing irrigation product (GRIPC) to construct the initial training sample pool. Next, to enhance the robustness and cover more irrigated candidates, we screened and introduced the irrigated croplands in three land use/cover maps (CCI-LC, GLC_FCS, and NLCD) to supplement the training data pool. Afterward, we utilized locally adaptive random forest classifiers and data cubes (MODIS-derived spectral indices, climatic and topographic variables) to generate irrigation maps in each province of China on GEE. The resulting map outperformed other current irrigation maps with an overall accuracy of 79.2% . The map also showed a reasonable consistency with statistical data at the province and prefecture levels, with the determination coefficient (R2) of 0.89 and 0.77, respectively. In total, we identified 87.04 million hectares of irrigated croplands in mainland China in 2015. Using the resulting map and water use statistics, we found a high correlation between irrigation area and agricultural water use in Northwest, Northeast, and South China, and a low correlation in North China Plain. This map is expected to serve national water resource management and assist decision-making in improving agricultural adaption to climate change.

Abstract Global food security is directly linked to human health and sustainable development. It is critical to comprehensively twig future changes in global food demand and progress in implementing the United Nations Sustainable Development Goals (SDGs), considering the transition of dietary patterns. We presented a global‐scale analysis quantifying the status of national food consumption and SDG performance in 2017, and the food demands and SDG index scores in 2030 were predicted under three healthy dietary patterns using 11 SDG indicators. High‐income nations scored well on most SDG indicators but poorly on food waste and the environment. Total global food demand was projected to be the highest under the national dietary guidelines pattern (12.69–13.37 million ton/d) and the lowest under the WHO healthy diet pattern (10.55–11.18 million ton/d). However, food demand under different dietary patterns varied among regions. Transitioning from current diets to healthy diets was projected to gain SDG index score for most countries. Strategies for shifts in dietary patterns to determine the tradeoff between global food security and sustainable development should be tailored to local conditions. The EAT‐Lancet dietary pattern is an optimal choice for determining the tradeoff between food security and sustainable development on a global scale. Given the current status of food access and nutrition, the National and the WHO dietary patterns are recommended for Asia and Africa, respectively.

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.

High aerosol levels pose severe air pollution and climate change challenges in Iran. Although regional aerosol optical depth (AOD) trends have been analyzed during the dusty season over Iran, the specific factors that are driving the spatio-temporal variations in winter AOD and the influence of meteorological dynamics on winter AOD trends remain unclear. This study analyzes the long-term AOD trends over Iran in winter during the period 2000–2020 using the updated Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Our results showed that the winter AOD exhibited a significant upward trend during the period 2000–2010 followed by a significant decrease during the period 2010–2018. We found that the winter AOD trends are important over this arid region due to multiple meteorological mechanisms which also affect the following spring/summer dusty period. Ground-based observations from Aerosol Robotic Network data (AERONET) in the Middle East region display trends comparable to those of both MERRA-2 and MODIS and indicated that aeolian dust and the meteorological dynamics associated with it play a central role in winter AOD changes. Furthermore, this study indicated that a significant downward trend in winter sea level pressure (SLP) during the early period (2000–2010) induced hot and dry winds which originated in the desert regions in Iraq and Arabia and blew toward Iran, reducing relative humidity (RH) and raising the temperature and thus promoting soil drying and dust AOD accumulation. In contrast, a significant increase in winter SLP during the late period (2010–2018) induced cold and wet winds from northwestern regions which increased RH and lowered the temperature, thus reducing dust AOD. This suggests that the changes in AOD over Iran are highly influenced by seasonal meteorological variabilities. These results also highlight the importance of examining wintertime climatic variations and their effects on the dust aerosol changes over the Middle East.

Climate warming is advancing spring leaf unfolding, but it is also reducing the cold periods that many trees require to break winter dormancy. Here, the authors show that 7 of 12 current chilling models fail to account for the correct relationship between chilling accumulation and heat requirement, leading to substantial overestimates of the advance of spring phenology under climate change.

Cement plays a dual role in the carbon cycle like a sponge. Here, the authors employ a dynamic model to quantify such sponge effect and concluded that deep decarbonization of the global cement cycle will require radical technology advancements and widespread deployment of material efficiency measures.

Abstract Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

The effects of winter chilling, spring forcing temperature, and photoperiod on spring phenology are well known for many European and North American species, but the environmental cues that regulate the spring phenology of East Asian species have not yet been thoroughly investigated. Here, we conducted a growth chamber experiment to test the effects of chilling (controlled by different lengths of exposure to natural chilling conditions), forcing temperature (12, 15, or 18°C) and photoperiod (14 or 10 h) on first flowering date (FFD) of six woody species (three shrubs and three trees) native to East Asia. The three-way analysis of variance (ANOVA) separately for each species showed that the effects of chilling and forcing temperature were significant for almost all species (P < 0.05). Averaged over all chilling and photoperiod treatments, the number of days until FFD decreased by 2.3–36.1 days when the forcing temperature increased by 3°C. More chilling days reduced the time to FFD by 0.7–26 days, when averaged over forcing and photoperiod treatments. A longer photoperiod could advance the FFD by 1.0–5.6 days, on average, but its effect was only significant for two species (including one tree and one shrub). The effects of forcing temperature and photoperiod interacted with chilling for half of the studied species, being stronger in the low chilling than high chilling treatment. These results could be explained by the theory and model of growing degree-days (GDD). Increased exposure to chilling coupled to a longer photoperiod reduced the GDD requirement for FFD, especially when plants grew under low chilling conditions. However, shrubs (except Viburnum dilatatum) had lower chilling and heat requirements than trees, suggesting that, by leafing out sooner, they engage in a more opportunistic life strategy to maximize their growing season, especially before canopy closure from trees' foliage. Our results confirmed the varying effects of these three cues on the flowering phenology of woody species native to East Asia. In future climate change scenarios, spring warming is likely to advance the spring phenology of those woody species, although the reduced chilling and shorter photoperiod may partly offset this spring warming effect.

Climate change could affect botanical tourism by altering the plant phenology (e.g., flowering and leaf coloring date) and the physical comfort of tourists. To date, few studies have simultaneously considered the influence of plant phenology and physical comfort on the travel suitability of botanical tourism. Taking Beijing as an example, this study used phenological data of 73 species from 1963 to 2017 to construct a phenological ornamental index (POI) according to the flowering and leaf coloring date of ornamental plant. The climate comfort index (CCI) of tourism was calculated by using meteorological data of the corresponding periods. Finally, the travel suitability index (TSI) was constructed by integrating the two indices (POI and CCI). The POI showed that the best period for spring flower viewing was from April 4 to May 10, while the best period for autumn leaves viewing was from October 11 to November 6 on average. According to the variation of the CCI within the year, the most comfortable period for spring tourism was matched with the best period for spring flower viewing (April 4 to June 1), but the most comfortable period for autumn tourism (September 4 to October 19) was earlier than the best period for autumn leaves viewing. The TSI indicated that the best periods for spring and autumn botanical tourism were April 7 to May 10 and October 10 to November 7, respectively. Based on the climate data under different scenarios (representative concentration pathways 4.5 and 8.5), we simulated the climate and phenological suitability for botanical tourism in the next thirty years. The results showed that the best period for spring botanical tourism during 2040–2050 was earlier and the period for autumn botanical tourism was later than that in the past 55 years. Meanwhile, the duration would shorten by 2–7 days for both seasons. This study provided a reference for assessing the impact of global climate change on the best season of botanical tourism.

‘Belt and Road Initiative’ (B&R) countries play critical roles in mitigating global carbon emission under the Paris agreement, but their driving factors and feasibility to reduce carbon emissions remain unclear. This paper aims to identify the main driving factors (MDFs) behind carbon emissions and predict the future emissions trajectories of the B&R countries under different social-economic pathways based on the extended STIRPAT (stochastic impacts by regression on population, affluence, and technology) model. The empirical results indicate that GDP per capita and energy consumption structure are the MDFs that promote carbon emission, while energy intensity improvement is the MDF that inhibits carbon emission. Population, as another MDF, has a dual impact across countries. The carbon emissions in all B&R countries are predicted to increase from SSP1 to SSP3, but emissions trajectories vary across countries. Under the SSP1 scenario, carbon emissions in over 60% of B&R countries can peak or decline, and the aggregated peak emissions will amount to 21.97 Gt in 2030. Under the SSP2 scenario, about half of the countries can peak or decline, while their peak emissions and peak time are both higher and later than SSP1, the highest emission of 25.35 Gt is observed in 2050. Conversely, over 65% of B&R countries are incapable of either peaking or declining under the SSP3 scenario, with the highest aggregated emission of 33.10 Gt in 2050. It is further suggested that decline of carbon emission occurs when the inhibiting effects of energy intensity exceed the positive impacts of other MDFs in most B&R countries.

This study assessed the aerosol climatology over Iran, based on the monthly data of aerosol optical depth (AOD) derived from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2) and the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS). In addition, sea level pressure, wind speed, temperature, relative humidity, precipitation, and soil moisture from the ERA5 reanalysis dataset were applied to investigate the climate-related effects on temporal AOD changes. Our analysis identified positive and negative AOD trends during 2000–2010 and 2010–2018, respectively, which are likely linked to aeolian dust changes. The dust-driven AOD trends were supported by changes in the Ångström exponent (AE) and fine mode fraction (FMF) of aerosols over Iran. During the early period (2000–2010), results of AOD-meteorology correlation analyses suggest reduced soil moisture, leading to increased dust emissions, whereas our results suggest that during the later period (2010–2018) an increase of soil moisture led to decreased AOD levels. Soil moisture appears to be a key factor in dust mobilization in the region, notably in southwestern Iran, being influenced by adjacent mineral dust sources. These phenomena were affected by large-scale sea level pressure transformations and the associated meteorology in the preceding winter seasons. Using a multiple linear regression model, AOD variability was linked to various meteorological factors in different regions. Our results suggest that climatic variations strongly affect the dust cycle, with a strong dependence on wintertime conditions in the region.

As the largest emitter in the world, China recently pledged to reach a carbon peak before 2030 and carbon neutrality before 2060, which could accelerate the progress of mitigating negative climate change effects. In this study, we used the Minimum Complexity Earth Simulator and a semi-empirical statistical model to quantify the global mean temperature and sea-level rise (SLR) response under a suite of emission pathways that are constructed to cover various carbon peak and carbon neutrality years in China. The results show that China will require a carbon emission reduction rate of no less than 6%/year and a growth rate of more than 10%/year for carbon capture capacity to achieve carbon neutrality by 2060. Carbon peak years and peak emissions contribute significantly to mitigating climate change in the near term, while carbon neutrality years are more influential in the long term. Mitigation due to recent China’s pledge alone will contribute a 0.16 °C–0.21 °C avoided warming at 2100 and also lessen the cumulative warming above 1.5 °C level. When accompanied by coordinated international efforts to reach global carbon neutrality before 2070, the 2 °C target can be achieved. However, the 1.5 °C target requires additional efforts, such as global scale adoption of negative emission technology for CO _2 , as well as a deep cut in non-CO _2 GHG emissions. Collectively, the efforts of adopting negative emission technolgy and curbing all greenhouse gas emissions will reduce global warming by 0.9 °C −1.2 °C at 2100, and also reduce SLR by 49–59 cm in 2200, compared to a baseline mitigation pathway already aiming at 2 °C. Our findings suggest that while China’s ambitious carbon-neutral pledge contributes to Paris Agreement’s targets, additional major efforts will be needed, such as reaching an earlier and lower CO _2 emission peak, developing negative emission technology for CO _2 , and cutting other non-CO _2 GHGs such as N _2 O, CH _4 , O _3 , and HFCs.

Climate change effects on global food security are not only limited to its effects on the yield of cereals but also their nutritional quality. However, climate change effects on crop nutritional quality, particularly grain protein concentration (PC) on a large geographical scale have not yet been quantified in China. For this purpose, we assessed the effects of three key climatic factors (temperature, precipitation, and solar radiation) on wheat PC in ten wheat-growing areas of China using a series of statistical models on a county-level PC dataset. The results demonstrated that the average PC in China from 2006 to 2018 ranged from 12.01% to 14.50% across the ten areas, with an obvious spatial difference pattern showing an increase in PC from south to north and from west to east. The sensitivity analysis indicated that PC showed a positive response to variation in the increasing temperature, and the PC of wheat grown in the Huanghuai area was less affected than the PC of wheat grown in other areas. Conversely, solar radiation posed negative effects on the PC in the southwestern area, whereas precipitation had intricate effects on the PC in all areas. Besides, the highest explanation of climate variability during five growth periods contributed 26.0%–47.6% of the PC variability in the northeastern area, whereas the lowest explanation of climate variability during five growth periods only accounted for 2.5%–3.7% of PC variability in the Yangtze River area. Our study further demonstrated that the effects of climate change on wheat grain PC in China were spatially heterogeneous with higher effects on PC in spring wheat-growing areas as compared to winter wheat-growing areas. We suggested that the northern and the northeastern area in China could be developed as alternative areas to produce wheat with high grain PC in the face of climate warming.

China has committed to ambitious targets to reduce its carbon emissions in the next decades, in order to combat climate change and improve the environment. The realization of the targets depends on the fair and effective mitigation plans of all provinces. However, with varying ecological and environmental conditions and social-economic development, it is a critical issue to quantify the provinces’ efforts equally. This paper proposed a comprehensive fE index in coordinating ecology, equity and economy, by accounting for carbon emissions and sinks to characterize provincial carbon emission status in China, from 2000 to 2017, which shows a spatial pattern of “boundary high, central low”. The provinces with higher fE value (>1.5) in boundary areas can be seen as “relative equality” provinces with good ecology circulation, equity and economic efficiency. The provinces with lower fE value (2 than their equity share by occupying the carbon emission space of other provinces in recent decades. These results could provide a reference for a provincial guide for carbon reduction and sustainable development of the low-carbon economy.

Research Highlights: This study looks at poplar canker caused by Cytospora chrysosperma as a geographical phenomenon, and it applies spatial statistics to reveal the pattern and aggregation effects of the disease on a large scale in time and space. The incidence area of poplar canker in Northeast China has spatial (spatiotemporal) aggregation effects, which emphasize the importance of coordinated prevention. The results of spatial and spatiotemporal clusters can guide specific regional prevention and indicate the possible predisposing factors, respectively. Background and Objectives: Poplar canker, a harmful forest biological disease that is widespread throughout Northeast China, brings enormous ecological and economic losses. The limited cognition of its spatiotemporal pattern and aggregation effects restricts the decision-making for regional prevention and the identification of disease-inducing conditions. This study aims to explore the spatiotemporal pattern and to detect the aggregation effects of the disease, trying to provide references for prevention. Materials and Methods: According to the incidence data of poplar canker reported by each county in Northeast China from 2002 to 2015, we mapped the distribution of the incidence rate in ArcGIS and performed retrospective scan statistics in SaTScan to detect the spatial and spatiotemporal aggregation effects of the incidence area. Results: The spatiotemporal pattern of poplar canker’s incidence rate presents the characteristic of “outbreak-aggregation-spread-stability.” The incidence area of the disease when we performed spatial aggregation scan statistics showed the primary cluster covering Liaoning province (LLR = 86469.86, p < 0.001). The annual spatial scan statistics detected a total of 14 primary clusters and 37 secondary clusters, indicating three phases of aggregation. The incidence area of disease also shows spatiotemporal aggregation effects with the primary cluster located around Liaoning province, appearing from 2009 to 2015 (LLR = 64182.00, p < 0.001). Conclusions: The incidence area of poplar canker presents significant characteristics of spatial and spatiotemporal aggregation, and we suggest attaching importance to the clues provided by the aggregation effects in disease prevention and identification of predisposing factors.

Spring phenology is a sensitive indicator of climate change and has substantial impacts on the carbon cycle. The global N cycle has been greatly disturbed by anthropogenic activities resulting in altered atmospheric N deposition worldwide. Research has been focused on the changes in the spring phenology and its covariations with climatic factors. However, the influences of N deposition on spring phenology have not been well documented to date. Herein, we investigated the effects of N deposition on the start of growing season (SOS) in continental United States (CONUS) during the years 1986–2015 using the normalized difference vegetation index (NDVI) datasets derived from both the third generation NDVI dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS). We observed that N deposition could only explain approximately 5% of temporal variation in SOS in CONUS. However, the sensitivities of SOS in response to unit change in both temperature (S _T ) and precipitation (S _P ) showed clear decreasing spatial patterns with increasing N deposition. The S _T generally decreased from −6 d/°C in low N deposition regions (4 kg ha ^−1 . Furthermore, the positive S _P also showed a continuously decreasing pattern with the increase in N deposition, but the negative S _P was gradually weakened when N deposition was >1.0 kg ha ^−1 . The results have important implications as it reveals the role of N deposition on spring plant phenology, and strongly suggest the consideration of N deposition effects when analyzing or predicting spring phenology in response to future climate change.

Volcanic eruptions, climate changes and their influences on crop harvests and social development are of increasing concern in science communities. Using a dataset of crop harvest scores of southwest China from 1730 to 1910, which was derived from the memorials to the emperors in the Qing Dynasty of China, reconstructed climate proxies and the chronology of large volcanic eruptions occurring between 10°S and 15°N, we analysed possible relationships between crop harvests, climate changes and volcanic eruptions. In addition, some archives of policies and measures related to crops and social development extracted from the chronicles were used to analyse social resilience when faced with poor harvests. The results show that crop harvests in the study area generally increased with fluctuations when there were less low-latitude large volcanic eruptions from 1730 to 1810. However, from 1811 to 1910, volcanoes at low latitudes erupted more frequently, which contributed to concurrent low temperature and drought. Meanwhile, the crop harvests showed a step-down decrease during the following periods of 1810s, 1850s, 1870s and 1890s. Though, the local social system was certainly resilient in facing of such climate and agriculture disasters, i.e. the local society remained stable without significant famine, large-scale migration or social unrest until 1911. The strong resilience of local social systems owed largely to various relieving measures, such as, building barns, exempting or reducing local taxes, allocating farmland to immigrants, and central government dominated grain purchasing and distribution to alleviate disasters.

Over the past four decades, large-scale ecological programs, including the ‘Great Green Wall Program’ (1978–present), ‘Grain for Green Program’ (1999–present), ‘Grassland Ecological Protection Program’ (2000–present), and ‘Beijing-Tianjin Sandstorm Source Control Project’ (2002–present), were launched to restore vegetation, to combat desertification, and to control dust storms in arid and semiarid China. The gross investments of these programs have exceeded 1700 billion RMB (∼260 billion USD, accounting for ∼1% GDP in the region) by now, however, the effects of these programs on vegetation restoration have not been settled. In this study, the vegetation indices, land-uses, and climatic factors were used to estimate the contribution of the above programs on vegetation restoration. The results showed that consistent vegetation restoration has occurred in about 45.3% of the vegetated areas in arid and semiarid China from 1982 to 2000, and the percentage decreased to 33.6% after 2000 despite more ecological programs have been launched. Changes in climatic factors (precipitation, temperature, solar radiation, and wind speed) and elevated atmospheric CO _2 concentration contributed more than 50% and 70% to vegetation restoration in periods of 1982–2000 and 2001–2015, respectively, however, the contribution rate of ecological programs kept stable at about 20%. Climate changes in the past forty years played a dominant role, although the ecological programs showed a noticeable effect on vegetation restoration. Further investment in ecological restoration practices might need to be critically evaluated on the cost-effectiveness.

Climate change, induced by human greenhouse gas emission, has already influenced the environment and society. To quantify the impact of human activity on climate change, scientists have developed numerical climate models to simulate the evolution of the climate system, which often contains many parameters. The choice of parameters is of great importance to the reliability of the simulation. Therefore, parameter sensitivity analysis is needed to optimize the parameters for the model so that the physical process of nature can be reasonably simulated. In this study, we analyzed the parameter sensitivity of a simple carbon-cycle energy balance climate model, called the Minimum Complexity Earth Simulator (MiCES), in different periods using a multi-parameter sensitivity analysis method and output measurement method. The results show that the seven parameters related to heat and carbon transferred are most sensitive among all 37 parameters. Then uncertainties of the above key parameters are further analyzed by changing the input emission and temperature, providing reference bounds of parameters with 95% confidence intervals. Furthermore, we found that ocean heat capacity will be more sensitive if the simulation time becomes longer, indicating that ocean influence on climate is stronger in the future.

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

To limit global warming to well below 2°C in accord with the Paris Agreement, countries throughout the world have submitted their Intended Nationally Determined Contributions (INDCs) outlining their greenhouse gas (GHG) mitigation actions in the next few decades. However, it remains unclear what the resulting climate change is in response to the proposed INDCs and subsequent emission reductions. In this study, the global and regional warming under the updated INDC scenarios was estimated from a range of comprehensive Earth system models (CMIP5) and a simpler carbon-climate model (MAGICC), based on the relationship of climate response to cumulative emissions. The global GHG emissions under the updated INDC pledges are estimated to reach 14.2∼15.0 GtC/year in 2030, resulting in a global mean temperature increase of 1.29∼1.55°C (median of 1.41°C) above the preindustrial level. By extending the INDC scenarios to 2100, global GHG emissions are estimated to be around 6.4∼9.0 GtC/year in 2100, resulting in a global mean temperature increase by 2.67∼3.74°C (median of 3.17°C). The Arctic warming is projected to be most profound, exceeding the global average by a factor of three by the end of this century. Thus, climate warming under INDC scenarios is projected to greatly exceed the long-term Paris Agreement goal of stabilizing the global mean temperature at to a low level of 1.5‐2.0°C above the pre-industrial. Our study suggests that the INDC emission commitments need to be adjusted and strengthened to bridge this warming gap.

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.

The authors wish to make the following corrections to this paper [...]

The purpose of this work is to present phenology as a valid indicator and methodology for monitoring and assessing the impact of climate change on plant-based tourist activities. Fruit-picking has become a popular rural tourism activity worldwide. However, fruit maturity dates (FMD) have been affected by climate change (CC), which has in turn profoundly affected fruit-picking tourism activities (FPTA). In this paper, phenological data on the FMD for 45 types of plants in 1980–2012, dates for more than 200 fruit-picking festivals, and data on monthly average air temperature in 1980–2013 were used to assess the impact of CC on FPTA by wavelet and correlation analyses. The findings indicated that the study area had been significantly affected by CC. Prevailing temperatures at one or three months prior have a decisive influence on FMD. Among the 11 plants directly related to FPTA, the FMD of four were significantly advanced, while 6-7 were significantly delayed owning to increased temperature. Of the 11 FPTA, only two had realized the impact of CC and had adjusted festival opening dates based on dynamic changes. However, a considerable number of festival activities remained fixed or scheduled on the weekends.

Based on the phenological data from China Phenological Observation Network, we compiled the phenological calendars of 3 phenological observation stations (Shanghai, Nanjing, and Hefei) in East China for 1987–1996 and 2003–2012 according to the sequences of mean phenophases. We calculated the correlated coefficient and the root mean square error (RMSE) between phenophases and the beginning of meteorological seasons to determine the beginning date of phenological season. By comparing new phenological calendars with the old ones, we discussed the variation of phenophases and their responses to temperature. The conclusions are as follows. (1) The beginning dates of spring and summer advanced, while those of autumn and winter delayed. Thus, summers got longer and winters got shorter. (2) The beginning time of the four phenological seasons was advancing during 1987–1996, while it was delaying during 2003–2012. (3) Most spring and summer phenophases occur earlier and most autumn and winter phenophases occur later in 2003–2012 than in 1987–1996. (4) The beginning time of phenological seasons was significantly correlated with temperature. The phenological sensitivities to temperature ranged from −6.49 to −6.55 days/°C in spring, −3.65 to −5.02 days/°C in summer, 8.13 to 10.27 days/°C in autumn, and 4.76 to 10.00 days/°C in winter.

In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000–2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

This study focuses on extreme precipitation changes in Xinjiang Province of Northwest China, which has experienced an increase in climate disasters in recent years. This paper investigates extreme precipitation events in Xinjiang, using 54 stations with daily precipitation records from the period 1961–2008. Different statistical tests and approaches were used to check the significance of trends of single and Xinjiang regionally aggregated precipitation series for intensity and in frequency. There were predominantly positive trends in annual maximum precipitation and a remarkable increment in the frequency of extreme precipitation over certain thresholds (from 10 to 40 mm). Although the series of frequencies exceeding thresholds had positive trends, only a minority were statistically significant. This lack of significance is because of the high variability of extreme precipitation in space and time. Thus, significant trends were evident when we assessed the extreme precipitation indicators of intensity and frequency at the regional level, both in intensity and frequency over thresholds, with a clearer signal in Xinjiang.

In order to understand past plant phenological responses to climate change in China (1963–2009), we conducted trends analysis of spring phenophases based on observation data at 33 sites from the Chinese Phenological Observation Network (CPON). The phenological data on first leaf date (FLD) and first flowering date (FFD) for five broad-leaved woody plants from 1963 to 2009 were analyzed. Since most phenological time series are discontinuous because of observation interruptions at certain period, we first interpolated phenological time series by using the optimal model between the spring warming (SW) model and the UniChill model to form continuous time series. Subsequently, by using regression analysis, we found that the spring phenophases of woody plants in China advanced at a mean rate of 0.18 days/year over the past 50 years. Changes of spring phenophases exhibited strong regional difference. The linear trends in spring phenophases were −0.18, −0.28, −0.21, −0.04, and −0.14 days/year for the Northeast China Plain, the North China Plain, the Middle-Lower Yangtze Plain, the Yunnan-Guizhou Plateau, and South China, respectively. The spatial differences in phenological trends can be attributed to regional climate change patterns in China.

Phenology-driven events, such as spring wildflower displays or fall tree colour, are generally appreciated by tourists for centuries around the world. Monitoring when tourist seasons occur using satellite data has been an area of growing research interest in recent decades. In this paper, a valid methodology for detecting the grassland tourist season using remote sensing data was presented. On average, the beginning, the best, and the end of grassland tourist season of Inner Mongolia, China, occur in late June (±30 days), early July (±30 days), and late July (±50 days), respectively. In south region, the grassland tourist season appeared relatively late. The length of the grassland tourist season is about 90 days with strong spatial trend. South areas exhibit longer tourist season.

Depending on the descriptions of crop yield and social response to crop failure/harvest from Chinese historical documents, we classified the crop yield of North China during 601–900 AD into six categories and quantified each category to be the crop yield grades. We found that the regional mean crop yield had a significant (P

Continuous satellite datasets are widely used in tracking vegetation responses to climate variability. Start of season (SOS), for example, can be derived using a number of methods from the time series of satellite reflectance data; however, various methods often produce different SOS measures which limit the application of satellite data in phenological studies. Therefore, we employed five methods to estimate SOS from the Advanced Very High Resolution Radiometer (AVHRR)/normalized difference vegetation index (NDVI) dataset. Subsequently, we compared the SOS with the ground-based first leaf date (FLD) of 12 deciduous broadleaved plant species at 12 sites of the Chinese Phenological Observation Network (CPON). The results show that the latitudinal patterns of five satellite-derived SOS measures are similar to each other but different from the pattern of ground phenology. For individual methods, the variability of SOS time series is significantly different from ground phenology except for HANTS, Polyfit, and Midpoint methods. The SOS calculated using the Midpoint method showed significant correlations with ground phenophases most frequently (in 47.1% of cases). Using the SOS derived from the Midpoint method, significantly earlier trends in SOS were detected in 50.7% of the natural vegetation area from 1982 to 2006.

Land use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationships, retrieve the most neighboring patches of a feature using the model for data combination, and thus guarantee the area of various land types in patch combination. (2) We establish patch features using nodes at the intersection of separate feature buffers to fill the bridge area to achieve feature aggregation and effectively control nonbridge area deformation during feature aggregation. (3) We simplify the narrow zones by dividing them from the adjacent feature buffer area and then amalgamating them into the surrounding features. This effectively deletes narrow features and meets the area requirements, better generalizes land use features, and guarantees simple and attractive maps with appropriate loads. (4) We simplify the feature sidelines using the Douglas-Peucker algorithm to effectively eliminate nodes having little impact on overall shapes and characteristics. Here, we discuss the model and algorithm process in detail and provide experimental results of the actual data.

To understand the effects of the land use/cover changes due to agricultural development on summer climate in Eastern China, four 12-year simulations using the WRF-SSiB model were performed. We found that agricultural development resulted in warming and rainy effects. In the middle to lower reaches of the Yellow River and the Yangtze River, the warming effects were approximately 0.6°C and resulted from increased surface net radiation and sensible heat fluxes. In Northeast China, the warming effects were very small due to increases in latent heat fluxes which resulted from the extensive conversion from grassland to cropland. The rainy effect resulted from increases in convective rainfall, which was associated with a warming surface in certain areas of the Yellow River and Yangtze River and a large increase in the surface moisture flux in Northeast China. Conversely, in the middle to lower reaches of the Yellow River and the Yangtze River, the grid-scale rainfall decreased because the climatological northward wind, which is moist and warm, was partially offset by a southward wind anomaly. These findings suggest that the agricultural development left footprints not only on the present climate but also on the historical climate changes before the industrial revolution.

Spatiotemporal simulation of tourist town growth is important for research on land use/cover change under the influence of urbanization. Many scholars have shown great interest in the unique pattern of driving urban development with tourism development. Based on the cellular automata (CA) model, we simulated and predicted the spatiotemporal growth of Sanpo town in Hebei Province, using the tourism urbanization growth model. Results showed that (1) average annual growth rate of the entire region was 1.5 Ha2 per year from 2005 to 2010, 4 Ha2 per year from 2010 to 2015, and 2.5 Ha2 per year from 2015 to 2020; (2) urban growth rate increased yearly, with regional differences, and had a high degree of correlation with the Euclidean distance of town center, traffic route, attractions, and other factors; (3) Gougezhuang, an important village center in the west of the town, demonstrated traffic advantages and increased growth rate since 2010; (4) Magezhuang village has the largest population in the region, so economic advantages have driven the development of rural urbanization. It showed that CA had high reliability in simulating the spatiotemporal evolution of tourist town, which assists the study of spatiotemporal growth under urbanization and rational protection of tourism resources.

Cellular automaton (CA) is an important method in land use and cover change studies, however, the majority of research focuses on the discovery of macroscopic factors affecting LUCC, which results in ignoring the local effects within the neighborhoods. This paper introduces a Local Land Use Competition Cellular Automata (LLUC-CA) model, based on local land use competition, land suitability evaluation, demand analysis of the different land use types, and multi-target land use competition allocation algorithm to simulate land use change at a micro level. The model is applied to simulate land use changes at Jinshitan National Tourist Holiday Resort from 1988 to 2012. The results show that the simulation accuracies were 64.46%, 77.21%, 85.30% and 99.14% for the agricultural land, construction land, forestland and water, respectively. In addition, comparing the simulation results of the LLUC-CA and CA-Markov model with the real land use data, their overall spatial accuracies were found to be 88.74% and 86.82%, respectively. In conclusion, the results from this study indicated that the model was an acceptable method for the simulation of large-scale land use changes, and the approach used here is applicable to analyzing the land use change driven forces and assist in decision-making.

Satellite-derived vegetation phenophases are frequently used to study the response of ecosystems to climate change. However, limited studies have identified the common phenological variability across different climate and vegetation zones. Using NOAA/Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) dataset, we estimated start of growing season (SOS) and end of growing season (EOS) for Chinese vegetation during the period 1982–2012 based on the Midpoint method. Subsequently, the empirical orthogonal function (EOF) analysis was applied to extract the main patterns of phenophases and their annual variability. The impact of climate parameters such as temperature and precipitation on phenophases was investigated using canonical correlation analysis (CCA). The first EOF mode of phenophases exhibited widespread earlier or later SOS and EOS signals for almost the whole country. The attendant time coefficients revealed an earlier SOS between 1996 and 2008, but a later SOS in 1982–1995 and 2009–2012. Regarding EOS, it was clearly happening later in recent years, mainly after 1993. The preseason temperature contributed to such spatiotemporal phenological change significantly. The first pair of CCA patterns for phenology and preseason temperature was found to be similar and its time coefficients were highly correlated to each other (correlation coefficient >0.7). These results indicate that there is a substantial amount of common variance in SOS and EOS across different vegetation types that is related to large-scale modes of climate variability.

This study analysed the changes in cropland greenness during 1982–2006 in the North China Plain (NCP) and investigated the warming/cooling effects of the greenness changes. The results show that while spring cropland greenness increased, early summer cropland greenness substantially decreased from 1982 to 2006. In contrast to the cooling and wetting effects of the greenness increase in spring, the greenness reduction in early summer had warming and drying effects. The cooling/warming effects of cropland greenness changes accounted for ∼47% of the spatial variance of daily maximum temperature ( T _max ) change in spring and ∼44% in early summer. The wetting/drying effects of cropland greenness changes accounted for ∼48% of the spatial variance of daily minimum specific humidity (SPH _min ) change in spring and ∼19% in early summer. The cooling–wetting/warming–drying effects mainly resulted from the distinct partitioning of surface net radiation between surface latent heat flux and sensible heat flux over cropland with different greenness. Canopy transpiration plays a dominant role. The increased (decreased) cropland greenness corresponds to high (low) transpiration rate, less (more) sensible heat flux and high (low) humidity, and consequently cooling–wetting (warming–drying) effects. In comparison, there was little change in surface net radiation, although surface albedo and emissivity had changed with greenness change.

A proxy precipitation index dataset for the period 501 - 2000 over eastern China (east of 105°E; 25 - 40°N approximately) was statistically derived from three existing datasets, which were reconstructed based on Chinese historical documents and instrument measurements. The index was then used to analyze decadal-to-centennial precipitation variability with a focus on three subregions, North China Plain (34 - 40°N approximately), Jiang-Huai area (31 - 34°N approximately) and Jiang-Nan area (25 - 31°N approximately). In addition, major drought/flood events considering severity, persistency, and spatial coverage were also identified. On the centennial time scales, precipitation variation in eastern China exhibited four dry epochs (500s - 870s, 1000s - 1230s, 1430s - 1530s and 1920s - 1990s) and three wet epochs (880s -990s, 1240s - 1420s and 1540s - 1910s), with multi-decadal dry/wet fluctuations within each epoch. However, variation showed strong regional differences, for example, opposite trends were found in the Jiang-Nan area and Jiang-Huai area during the 11 - 13th centuries and in the North China Plain and Jiang-Nan area since the 16th century. The data also showed 16 drought and 18 flood events in eastern China, with the most severe drought event occurring in 1634 - 1644. Droughts dominated in the 12 - 14th centuries, but since the middle of the17th century eastern China has been more subject to flooding. The severity of floods during the 20th century was comparable in intensity to historical times, but the droughts were usually less severe.

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. [ABSTRACT FROM AUTHOR]