Northeastern China (NEC) is the largest grain base in China. Improving understanding of the effect of climate change on grain production over NEC is conducive to providing immediate response strategies for grain production. In this study, the relationships of the maize production with the dry state during the different maize growth stage have been investigated using the year-to-year increment method. Results showed that the severe drought that occurred from the jointing to maturity period have exerted severe effects on the maize growth. Further analysis indicated that the sea surface temperature (SST) anomalies over North Atlantic and Maritime Continent in later spring are the important factors affecting the summer droughts over NEC. The late spring SST anomaly over North Atlantic can excite the Rossby waves from the western North Atlantic and propagate eastward to NEC. The snow anomaly over western Siberia in late spring and the soil moisture anomaly over NEC in summer are key factors linking the SST anomaly to drought over the NEC. On the other hand, the Maritime Continent SST anomaly in late spring can modulate the activity of the East Asian jet stream via the East AsiaPacific (EAP) teleconnection, which can provide the favorable conditions for the soil moisture reduction over NEC. Eventually, a predictive model for maize yield over NEC is successfully developed by using the predictive indices of the North Atlantic and the Maritime Continental SST during late spring. Both the cross-validation and independent sample tests show that the calibrated prediction model is robust and exhibits high skill in predicting maize yield over NEC.

Global warming leads to the melting of permafrost, affects changes in soil microbial community structures and related functions, and contributes to the soil carbon cycle in permafrost areas. Located at the southern edge of Eurasia's permafrost region, the Greater Khingan Mountains are very sensitive to climate change. Therefore, by analyzing the bacterial community structure, diversity characteristics, and driving factors of soil profiles (active surface layer, active deep layer, transition layer, and permafrost layer) in this discontinuous permafrost region, this research provides support for the study of the carbon cycling process in permafrost regions. The results show that the microbial diversity (Shannon index (4.81)) was the highest at 0-20 cm, and the Shannon index of the surface soil of the active layer was significantly higher than that of the other soil layers. Acidobacteria and Proteobacteria were the dominant bacteria in the active layer soil of the permafrost area, and Chloroflexi, Actinobacteria, and Firmicutes were the dominant bacteria in the permafrost layer. Chloroflexi made the greatest contribution to the bacterial community in the permafrost soil, and Bacteroidota made the greatest contribution to the bacterial community in the active surface soil. The structure, richness, and diversity of the soil bacterial community significantly differed between the active layer and the permafrost layer. The number of bacterial species was the highest in the active layer surface soil and the active layer bottom soil. The difference in the structure of the bacterial community in the permafrost soil was mainly caused by changes in electrical conductivity and soil-water content, while that in the active layer soil was mainly affected by pH and soil nutrient indices. Soil temperature, NO3--N, and pH had significant effects on the structure of the bacterial community. The active layer and permafrost soils were susceptible to environmental information processing and genetic information processing. Infectious disease: the number of bacterial species was significantly higher in the surface and permafrost layers than in the other layers of the soil. In conclusion, changes in the microbial community structure in soil profiles in discontinuous permafrost areas sensitive to climate change are the key to soil carbon cycle research.

Global warming has led to permafrost degradation worldwide. The Qinghai-Tibet Plateau (QTP) hosts most of the world's alpine permafrost, yet its impending changes remain largely unclear, thereby affecting regional hydrological and ecological processes and the global carbon budget. By employing a land surface model adapted to simulate frozen ground, and using state-of-the-art multi-model and multi-scenario data from the Coupled Model Intercomparison Project Phase 6, changes in permafrost distribution and its thermal regimes on the QTP are systematically predicted under various shared socioeconomic pathways (SSPs). Projections for SSP2-4.5, SSP3-7.0, and SSP5-8.5 show that most of the continuous permafrost region of the QTP will persist through 2050. Much of the permafrost is likely to degrade in the late 21st century, with projected area losses of 44 +/- 4%, 59 +/- 5%, and 71 +/- 7%, respectively, by 2100. In particular, the Three Rivers Source region in the central eastern part of the QTP is a key area of permafrost degradation, where permafrost is most vulnerable and degradation occurs earliest. The mean annual ground temperature of QTP permafrost will increase by 0.8 +/- 0.2 degrees C, 2.0 +/- 0.3 degrees C, and 2.6 +/- 0.3 degrees C under SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively, and the active layer thickness will increase by 0.7 +/- 0.1 m, 1.5 +/- 0.3 m, and 3.0 +/- 1.0 m, respectively. The surviving permafrost under SSP3-7.0 and SSP5-8.5 will be thermally unstable, which is a clear warning sign of complete disappearance. The analysis of permafrost sensitivity to climate change signifies that alpine permafrost on the QTP has low resilience to climate change, in contrast to permafrost in pan-Artic high latitudes.

Drought is a complicated and costly natural hazard and identification of critical drought factors is critical for modeling and forecasting of droughts and hence development of drought mitigation measures (the Standardized Precipitation-Evapotranspiration Index) in both space and time. Here we quantified relationships between drought and 23 drought factors using remote sensing data during the period of 2002-2016. Based on the Gradient Boosting Algorithm (GBM), we found that precipitation and soil moisture had relatively large contributions to droughts. During the growing season, the relative importance of Normalized Difference Water Index (NDWI-7) for SPEI3, SPEI6, SPEI9, and SPEI12 reached as high as 50%. However, during the non-growing season, the Snow Cover Fraction (SCF) had larger fractional relative importance for short-term droughts in the Inner Mongolia and the Loess Plateau which can reach as high as 10%. We also compared Extremely Randomized Trees (ERT), H2O based Deep Learning (Model developed by H2O.deep learning in R H2O.DL), and Extreme Learning Machine (ELM) for drought prediction at various time scales, and found that the ERT model had the highest prediction performance with R-2 > 0.72. Based on the Meta-Gaussian model, we quantified the probability of maize yield reduction in the North China Plain under different compound dry-hot conditions. Due to extreme drought and hot conditions, Shandong Province in North China had the highest probability of >80% of the maize yield reduction; due to the extreme hot conditions, Jiangsu Province in East China had the largest probability of >86% of the maize yield reduction. (C) 2021 Elsevier B.V. All rights reserved.

Thin sandstone reservoirs of the fan delta front sub-facies occur in the early Neogene (Miocene) series of the Aketao (Akto) structural belt within the Kunlun piedmont zone of the Tarim Basin. Oil and gas reservoirs in this area correspond to stratigraphic traps. However, owing to the low density of the 2D seismic survey grid deployed in the Aketao belt, inferior seismic data quality, and lack of well logging data, reservoir prediction in this area suffers from a multiplicity of problems and it is difficult to effectively identify sand bodies. Here, a new research approach is proposed involving the use of 3D seismic, well logging, and drilling data from a neighboring highly-explored 3D seismic survey area as a reference for the 2D seismic interpretation of the non-drilled Aketao survey area. Moreover, this approach is integrated with forward modeling and the inversion of post-stack seismic data to identify sand bodies. A comparison of the seismic reflection characteristics clarifies that these 3D and 2D seismic survey areas share similar sedimentary environments. Forward modeling confirms their similar reservoir characteristics, while the reservoir distribution in the 2D seismic survey area is effectively mapped via the inversion. The results show that for a 2D seismic survey area characterized by a low degree of hydrocarbon exploration and appraisal, and a lack of well logging data, the proposed approach can confirm the sedimentary characteristics that correspond to the seismic reflection characteristics, and can quantitatively map the reservoir thickness.

Satellite data on methane concentration in the lower troposphere and the dynamical permafrost model are used to analyze methane emissions in the permafrost zone. The sources of methane generation in different biochemical conditions in the river valleys, thermokarst lakes, wetlands, and lowlands are studied. The statistical relationships between their intensity and air temperature, precipitation, active layer thickness, and permafrost temperature are evaluated. The CMIP5 ensemble climate projection is used to estimate methane emission in the permafrost regions for the mid-21st century. Numerical experiments with the INM-CM48 Earth system model demonstrated that the projected 20 Tg/year increase in the methane emission will lead to less than 0.05 degrees C global temperature rise. The uncertainty analysis of the results is accomplished and an alternative conceptual model of abrupt threshold changes in methane emission is proposed.

Dust aerosols play key roles in affecting regional and global climate through their direct, indirect, and semi-direct effects. Dust events have decreased rapidly since the 1980s in East Asia, particularly over northern China, primarily because of changes in meteorological parameters (e.g. surface wind speed and precipitation). In this study, we found that winter (December-January-February) Arctic amplification associated with weakened temperature gradients along with decreased zonal winds is primarily responsible for the large decline in following spring (March-April-May) dust event occurrences over northern China since the mid-1980s. A dust index was developed for northern China by combining the daily frequency of three types of dust event (dust storm, blowing dust, and floating dust). Using the empirical orthogonal function (EOF) analysis, the first pattern of dust events was obtained for spring dust index anomalies, which accounts for 56.2% of the variability during 1961-2014. Moreover, the enhanced Arctic amplification and stronger Northern Hemisphere annular mode (NAM) in winter can result in the anticyclonic anomalies over Siberia and Mongolia, while cyclonic anomalies over East Europe in spring. These results are significantly correlated with the weakened temperature gradients, increased precipitation and soil moisture, and decreased snow cover extent in the mid-latitude over Northern Hemisphere. Based on the future predictions obtained from the Fifth Climate Models Intercomparison Project (CMIP5), we found that the dust event occurrences may continually decrease over northern China due to the enhanced Arctic amplification in future climate.