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In a cluster-randomized trial, villages were assigned in a 1:1 ratio to use a salt substitute (75% sodium chloride and 25% potassium chloride by mass) or regular salt. Among persons who had a history of stroke or were 60 years of age or older and had hypertension, rates of stroke, major cardiovascular events, and death were lower with the salt substitute, which had no apparent serious adverse effects.

Based on the reconstructed temperatures, precipitation changes, and occurrences of extreme climate events, together with historical records on fiscal deterioration, food crises, and the frequencies of popular unrest, rebellions and wars, we identified three principal ways in which climate change contributed to the collapse in the Ming dynasty. Firstly, cooling, aridification, and desertification during a cold period destroyed the military farm system, which was the main supply system for the provisioning of government troops on the northern frontiers; these impacts increased the military expenditure from 64 % of total government expenditure in 1548–1569 to 76 % in 1570–1589 and thus aggravated the national fiscal crisis that occurred during the late Ming dynasty. Secondly, climate deterioration (e.g., cooling, aridification, and an increase in the frequencies of frost- and drought-related disasters, etc.) led to a 20–50 % reduction in the per capita production of raw grain in most areas of China, which resulted in widespread food crises and exacerbated the vulnerability of social structures during the last several decades of the Ming dynasty. Thirdly, the severe droughts occurring in 1627–1643 were a key trigger to the peasantry uprising. These droughts also played a significant role to promote the peasantry uprising, especially reviving the peasantry troops by recruitment of famine victims when they nearly perished in 1633 and 1638, and severely disrupting the food supply for the government troops, resulting in the final defeat of the government troops by the peasantry troops. This study contributes to an understanding of the climate-related mechanisms behind the collapse of the Ming dynasty, and provides a historical case study that enhances our understanding of the nature of interactions between climate change and social vulnerability.

Based on historical records and scores of the grain harvest in the Qing Dynasty, we reconstruct the spatial–temporal distribution and disaster severities of drought and flood disasters in eastern China in 1751, and we summarize the social countermeasures to the disasters at that time. In the summer of 1751, the regions south of the Yangtze River in China experienced droughts, while northern China suffered floods caused by excessive rainfall. From 14 June to 11 August, the entire Zhejiang Province and its surrounding areas were impacted by droughts, resulting in the most serious crop failure in Zhejiang Province from the 18th to the nineteenth century. However, the floods in northern China did not seriously affect agricultural production. In response to the droughts, the government implemented relief measures such as exempting taxes in disaster areas, using raw grains in warehouses to help famine refugees, and transferring rice from bumper harvest areas to disaster areas. All the disaster relief measures provided Zhejiang Province with enough rice to feed 1.6325 million people for 1 year. In addition, the government also took some other measures to assist in disaster relief. The series of measures adopted by the government mitigated the impact of the drought on society, and the influence of the drought ended completely on 11 May 1752. This study improves our understanding the spatial–temporal distribution of a climatic disaster based on historical records, and it explores the adaptation of human society to climatic disasters.

This is an extended editors’ commentary on the topical collection “Historical and recent change in extreme climate over East Asia”, which collects a total of 15 papers related to the change and variability of extreme climate events in East Asia over the last few hundreds years. The extreme climate events are broadly classified into three categories: temperature and extreme warmth/coldness, precipitation and floods/droughts and western North Pacific typhoons. This commentary briefly summarizes the main findings presented in each paper in this topical collection, and outlines the implications of these findings for monitoring, detecting and modeling of regional climate change and for studying climate change impacts and adaptability. It also assesses the uncertainties of these studies, as well as the remaining knowledge gaps that should be filled in the future. One solid conclusion we can draw from these studies is that there was a marked decadal to multi-decadal variability of extreme climate events in East Asia in recent history, and the extreme events as observed during the last decades of the instrumental era were still within the range of natural variability except for some of those related to temperature. More severe and enduring droughts occurred in the early 20 th century or the earlier periods of history, frequently leading to great famines in northern China. Uncertainties remain in reconstructing historical extreme climate events and analyzing the early instrumental records. Further research could focus on the improvement of methodology in proxy based reconstruction of multi-decadal variations of surface air temperature and precipitation/drought, the recovery, digitization, calibration and verification of the early instrumental records, and the mechanisms of the observed multi-decadal variability of extreme climate in the region.

Proxy-based reconstructions are essential for investigating precipitation change in Asia on multidecadal to centennial timescales. Thus far, a total of four gridded precipitation/drought datasets for the past half millennium have been developed, including reconstructed Asian summer precipitation (RAP), May–September precipitation in China (IGGPRE), warm season precipitation for Asia (WSP), and Monsoon Asia Drought Atlas (MADA). This study uses some independent evidence to evaluate and compare the four reconstructions and then examine the spatial patterns of precipitation under different phase combinations of the Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO). The results suggest that RAP and IGGPRE capture most variance of observations in eastern China, WSP performs better in central Asia and India, while MADA shows skill over Mongolia and the eastern Tibet Plateau. When proxies are evenly distributed in space, increasing the sample size contributes more to the explained variance than increasing the signal-to-noise ratio. In terms of the responses to the multidecadal oscillations, the spatial correlation coefficients are all negative between pairs of patterns, with only one of the oscillations residing in opposite phase. This means that all four reconstructed datasets capture the reverse spatial pattern when the phase of multidecadal oscillation changes. The WSP is the only dataset that shows positively correlated spatial patterns when the AMO and the PDO reverse together. The RAP and IGGPRE are more sensitive to the AMO than to the PDO, because the negative correlations caused by the AMO exhibit higher confidence levels. The spatial pattern in history is similar to that in the twentieth century when the PDO and AMO are both in positive phase, which indicates that the mechanism in this situation is stable over the long-term period.

An extreme drought occurred from 1876 to 1878 in most regions of northern China, leading to a series of social impacts, including harvest failures, price inflation, and population migration. Most regions of the Northern Hemisphere concurrently experienced extreme drought. Here, we use reconstructed high-resolution hydroclimatic (Palmer Drought Severity Index/precipitation) datasets and investigate the seasonal–annual hydroclimatic spatial patterns and drought intensity in North China from 1876 to 1878. Furthermore, we select combined sea surface temperature (SST) modes with positive Indian Ocean Dipole (IOD)/Pacific Decadal Oscillation (PDO) and El Niño conditions from the 1200-year control run simulations of HadCM3 to determine the possible causes of this severe drought. The extent and intensity of the selected SST modes are similar to those in the Pacific and Indian oceans during the 1876–1878 period from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature (ERSST) dataset. The results show that the large-scale drought of 1876–1878 is mainly driven by El Niño and a positive PDO, while the effect of IOD is not significant. El Niño may trigger the circumglobal teleconnection of the Northern Hemisphere. And, the meridional disturbance of the eastward Rossby wave train at mid-latitudes may change the intensity of the troughs and ridges and further block water vapor transport from ocean to land.

The general characteristics of climate changes over the past 2000 years in China,regional differences and uncertainties were analyzed based on the recently peer-reviewed high time-resolution climatic reconstructions.The results showed that there exists four warm periods of the temperature variation in China since the Qin Dynasty,including the western and eastern Han Dynasties（200 BC-AD 180）,the Sui and Tang dynasties（541-810）,the Song and Yuan dynasties（931-1320）,and the 20th century,and three cold phases involving the Wei,Jin,and North-South Dynasties（181-540）,the late Tang Dynasty（811-930）,and the Ming and Qing dynasties（1321-1920）.The Song and Yuan warm period is consistent with the Medieval Warm Period over the Northern Hemisphere,and the cold phases of the North-South Dynasties and the Ming and Qing dynasties are paralleled to the Dark Ages Cold Period and the Little Ice Age,respectively.The 13th-15th century could be a shift to the wet condition of the climate,and the low precipitation variability is exhibited in western China prior to 1500.In the context of the climate warming,the pattern of the drought in north and flood in south is prevalent over the eastern China.In addition,the published reconstructions have a high level of confidence for the past 500 years,but large uncertainties exist prior to the 16th century.

In this study, the hierarchical clustering algorithm was used to cluster the summer heat waves (HWs) in northern East Asia (NEA) into wave-train HW and blocking HW. In the past 38 years, the wave-train HWs have increased significantly, while the blocking HWs have not shown a visible trend. The wave-train index and the blocking index are proposed to objectively identify the HW type after capturing the circulation characteristics of two kinds of HWs. When a wave-train HW occurs in NEA, simultaneous HW will occur in Eastern Europe. However, when a blocking HW occurs in NEA, a negative temperature anomaly will take place in Eastern Europe. The results of numerical simulations using the linear baroclinic model indicate that the wave-train HWs and the blocking HWs are related to the heating anomaly over southern Greenland and the cooling anomaly over the Barents Sea, respectively.

Changes in climate extremes under 1.5 °C and 2 °C global warming may impact agricultural production across Central Asia. We used the simulated daily data of average temperature, maximum temperature, minimum temperature, and precipitation provided by the Inter-Sectoral Impact Model Intercomparison Project and analyzed the current climate status and future projected changes of a set of climate extreme indices related to agricultural production under 1.5 °C and 2 °C global warming. In addition, the possible impacts of climate change on agricultural production in Central Asia were discussed. The results show that the annual mean temperature in Central Asia will increase by 1.48 °C and 2.34 °C at 1.5 °C and 2 °C warming levels, respectively, compared to the base period (1986–2005), and the increasing trends are significant at the α = 0.01 level for all grids. The number of warm days and growing season length will increase. Under the 1.5 °C scenario, the mean annual total precipitation (PRCPTOT) and heavy precipitation (R95P) will experience increases of 7.68% and 26.55%, respectively, and the consecutive dry days (CDD) will be reduced by 1.1 days. However, the standardized precipitation evapotranspiration index (SPEI) shows significant drought conditions in most of Central Asia (more than 60%). Under the 2 °C scenario, there will be a 3.89% increase in PRCPTOT and a 24.78% increase in R95P. Nevertheless, accompanying the increase in CDD (0.8 day) and the decrease in SPEI, drought conditions will be further exacerbated. These results indicate that Central Asia is likely to face more severe ecological problems in the future, which will threaten the regional agricultural production and the food security. Therefore, adaptation strategies should be implemented immediately to mitigate the negative impacts of climate change on Central Asia’s agriculture.

PurposeTo develop and validate a machine learning model based on radiomic features derived from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images to preoperatively predict the pathological grade in patients with pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative 18F-FDG PET/CT scan between May 2009 and January 2016 were included in this retrospective study. The cohort of patients was divided into two separate groups for the training (99 patients) and validation (50 patients) in chronological order. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, and the XGBoost algorithm was used to build a prediction model. Conventional PET parameters, including standardized uptake value, metabolic tumor volume, and total lesion glycolysis, were also measured. The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC).ResultsThe prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set.ConclusionThe model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative 18F-FDG PET/CT radiomics features.