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A massive eruption of Okmok volcano in Alaska has been recently discovered and precisely dated to have occurred in 43 BCE. Some Chinese climate records of 43–33 BCE in historical documents have been found that provide descriptions of observed environmental abnormities that appear to be consistent with the anticipated changes due to volcanic climate forcing. We provide full translation with discussions of the Chinese climate records that may be related to the Okmok eruption in this paper. We have converted ancient Chinese calendar dates to modern Gregorian dates and provided the latitudes and longitudes of the geographical locations mentioned in the records. We believe the detailed information contained in these records will be useful for further research on the climate impact of volcanic eruptions. [ABSTRACT FROM AUTHOR]

In this paper, climate change in the Jinping area is investigated. The climate change trend in the Jinping area is studied by plotting the porosity value of the carbonate rocks as a curve. By comparing the curve established using the climate change data from published articles, it is found that the B value curve obtained using the saddle line is the closest to the curve established using the climate change data from published articles. This shows that the carbonate porosity in the Jinping area obtained using an image analysis technique can be used for climate change research. [ABSTRACT FROM AUTHOR]

The impact and feedback of climate--vegetation interaction on the potential habitat of plants is one of the focuses of terrestrial ecosystem and global climate change research. Based on 246 occurrence records of S. chamaejasme and 28 environmental variables (climate, topography and soil), we predicted and analyzed the suitable habitat distributions and shifts of S. chamaejasme under current and future (by 2050 and 2070) climate scenarios using the maximum entropy (Maxent) model. The Area Under the receiver operating characteristics Curve (AUC) was used to evaluate the model performance. The key environmental factors were screened by Jackknife tests. The results showed the following: 1) AUC values indicated better performance of MaxEnt modeling. 2) Altitude, annual precipitation, extreme moisture conditions (including precipitation of the wettest and the driest month) and mean temperature of the coldest quarter were the most predominant environmental factors influencing S. chamaejasme's habitat suitability. 3) In the current scenario, S. chamaejasme distributed in Qinghai--Tibet plateau, Loess Plateau, Inner Mongolia plateau and Yungui plateau (covering 44.16% of total land of China), especially in Sichuan (37.25%) and Gansu (34.65%) province. 4) With climate change, an increasing trend for the distribution (except in 2050_RCP2.6 and 2050_RCP8.5) had taken place in the area of all suitable classes comparing current and future scenarios overall, while the trends of variation are insignificant. At the same time, the centroid of highly suitable distribution regions shifted to northeast China, and the distribution regions moderately extended to the south. This paper may provide a deep insight for further research and practice management on S. chamaejasme. [ABSTRACT FROM AUTHOR]

Research on precipitation regularity in the past 120 years is an important link in analyzing the precipitation characteristics of watersheds. This paper systematically analyzes the characteristic changes of centennial precipitation data in the Haihe River basin with the help of CRU data, PCI, SPI, and the Pearson type III curve. The results show that the spatial and temporal distribution of precipitation in the Haihe River basin has a more obvious inconsistency. The temporal distribution shows the characteristics of relatively stable in the early period and increasing fluctuation in the later period, the concentration of precipitation gradually decreases, and the overall drought level decreases. The spatial distribution shows a general pattern of gradually decreasing from southwest to northeast, the overall trend of summer precipitation changes from stable to north–south extremes, and the distribution probability of extreme precipitation events in the basin decreases from southeast to northwest, while the drought-prone area transitions from the northeast to the west and southwest of the basin. Under the influence of both climate change and human activities, the seasonal distribution of precipitation tends to be average, the area affected by extreme precipitation rises, and the arid area shifts to the inland area. [ABSTRACT FROM AUTHOR]

This research focuses on a bibliometric analysis of research on aerosols' impact on the glaciers in the Himalayan glacier region published in journals from all subject categories based on the Science Citation Index Expanded, collected from the Web of Science and Scopus database between January 2002 and April 2022. The indexing phrases like "aerosol," "glacier," and "snow" are commonly used terms and have been utilized to collect the related publications for this investigation. The document selections were based on years of publication, authorship, the scientific output of authors, distribution of publication by country, categories of the subjects, and names of journals in which scholarly papers were published. The number of articles on aerosols accelerating the melting of glaciers shows a notable increase in recent years, along with more glacier melting results from countries involved in climate science research. People's Republic of China (382) was the country with the highest publication output on aerosols impacting the melting of glaciers. The USA (367) was the most cited country, with about 17,500 total citations and 80.40 average citations per year from January 2002 to April 2022. The results reveal that research trends in the glaciers on aerosols' impact on the glaciers have been attractive in recent years, and the number of articles in this field keeps increasing fast. This study offers opportunities to track research trends, identify collaboration prospects, and inform climate policy. Integrating data sources and engaging the public will further enhance the impact and relevance of this critical research field. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Journal of East Asian Studies is the property of National Taiwan Normal University, College of International Studies & Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods. [ABSTRACT FROM AUTHOR]

The driving force of climate change in the monsoon margin is complex, making it a key area for regional and global climate change research. Palaeohydrological studies in the monsoon margin have increased the resolution of research in the long term, transitioning from qualitative to quantitative studies to comprehend climate change processes, patterns, and mechanisms. Testate amoebae (TA) in peat sediments are used as a proxy indicator organism for quantitative reconstruction of palaeohydrology. Thus, their community changes are directly related to precipitation, and widely used to reconstruct the patterns of summer precipitation globally. We investigated TA species and reconstructed palaeohydrological changes in the Greater Khingan Mountains' Hongtu (HT) peatland, located in the East Asian Summer Monsoon (EASM) margin. The result showed that the most abundant TA species were Assulina muscorum (12.4 ± 5.0%) and Nebela tincta (8.9 ± 4.9%) in the HT peat core. The increase in dry indicator species (e.g., A. muscorum and Alabasta militaris) indicated a drying pattern in the HT peatland since 150 cal yr BP. Principal component analysis (PCA) explained 47.6% of the variation in the selected TA assemblages. During 400 to 250 cal yr BP, PCA axis 1 scores ranged from 0.2 to −1.3 (reflecting a drier climate), associating with the Little Ice Age. The paleohydrology of the northern part of the Greater Khingan Mountains was mainly controlled by the EASM, which was associated with changes in North Atlantic Sea surface temperature and solar radiative forcing. The apparent drying pattern may be the result of the gradual intensification of anthropogenic activities and the increase in EASM intensity. [ABSTRACT FROM AUTHOR]

Geochemical mapping is a crucial tool that can provide valuable insights for a wide range of applications, including mineral resources prospecting, environmental impact assessment, geological process understanding, and climate change research. Despite its significance, geochemical mapping requires spatial modeling based on sparse, heterogeneous, and potentially inaccurate data sets. Moreover, the underlying geological processes are often imperfectly understood. Therefore, uncertainty quantification (UQ) is vital in geochemical mapping to ensure accurate and reliable results, ultimately facilitating well‐informed decision‐making. In this contribution, we distinguish two primary types of uncertainties: systemic and stochastic. We identify the key sources of uncertainties in geochemical mapping and review the techniques that have been employed or hold potential for uncertainty quantification, communication, visualization, and sensitivity analysis. This contribution also illustrates the general procedure of UQ in geochemical mapping by a case study of mapping geochemical anomalies associated with gold mineralization in northwestern Sichuan Province, China. We also explore potential strategies for mitigating the critical uncertainties, such as gathering more geochemical data, developing more effective models, enhancing our understanding of the geochemical dispersion process, or leveraging other thematic information or knowledge. Future research should prioritize addressing underexplored uncertainties and implementing more practical applications to validate the UQ procedure in geochemical mapping. Plain Language Summary: Geochemical mapping is a vital tool for various applications such as finding minerals, assessing environmental impact, understanding geological processes, and researching climate change. However, the challenges lie in dealing with limited, varied, and potentially inaccurate data coupled with an imperfect understanding of geological processes. To ensure accurate and reliable results, we emphasize the importance of uncertainty quantification in geochemical mapping. Our review distinguishes between two main uncertainties, systemic and stochastic, and identifies their key sources in geochemical mapping. We also explore techniques for uncertainty quantification, communication, visualization, and sensitivity analysis. Using a case study in northwestern Sichuan Province, China, we illustrate the general procedure of uncertainty quantification by mapping geochemical anomalies related to gold mineralization. To address critical uncertainties, we suggest strategies such as gathering more data, improving models, and enhancing our understanding of geochemical dispersion. Future research should focus on exploring underexplored uncertainties and implementing more practical applications to validate the procedure of uncertainty quantification in geochemical mapping. Key Points: Geochemical mapping is intricate due to its scale‐dependent and heterogeneous natureUncertainty quantification is an essential part of geochemical mappingThe Monte‐Carlo framework is effective to account for multiple crucial sources of uncertainty manifested in geochemical mapping [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Mycosystema is the property of Mycosystema Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the development of machine learning, especially deep learning, has provided new methods and ideas for current climate research. Stacked auto-encoder based on deep learning are used to perform nonlinear down-scaling to compress the degree of freedom of climate variables in the early stage. And climate predictors features are extracted from the summer precipitation in four regions in eastern China, from which key climate predictors affecting summer precipitation in each region are identified. On this basis, a variety of regression methods including random forest in machine learning method were used to construct prediction models for key climate predictors in each region. The best model parameters were determined by the sensitivity test of model parameters to forecast results. Several years of predictions suggest that the method for the forecast of precipitation in eastern China has a very high skill, especially in southern China. The results show that the anomaly consistency of the proposed model in regional prediction is better than that of the model. Compared with the mainstream model, the prediction results in South China can be improved by more than 10%. The method has a good application prospect for summer precipitation prediction in eastern China. [ABSTRACT FROM AUTHOR]

The simultaneous occurrence of droughts and floods in neighboring regions amplifies the threats posed by droughts and floods individually. Nonetheless, few studies have been conducted to investigate the simultaneous occurrence of drought and flood events. Here we explore the spatiotemporal characteristics and the shift pattern of droughts and pluvials over Eastern China from a three‐dimensional perspective, using the self‐calibrated Palmer Drought Severity Index and the Climate Research Unit data set as well as four regional climate model simulations. We find that Eastern China experienced droughts and pluvials simultaneously in different locations during boreal summer, and it is projected to simultaneously experience more frequent and more intense droughts and pluvials under a warming climate. Specifically, we investigate the pattern of more pluvials in Southeast China and more droughts in Northeast China for the historical period of 1975–2004. This pattern dynamically evolves under climate warming: the pluvial‐dominated regime shifts from Southeast to Northeast China, while the drought‐dominated regime shifts from Northeast to Southeast China. The weakening strength of the western Pacific subtropical high and a northward displacement of the monsoon rain belt may both contribute to the pattern of more pluvials in Northeast China and more droughts in Southeast China. These findings provide insights into the development of adaptation strategies and emergency response plans for enhancing society's resilience to the spatial co‐occurrence of dry and wet extremes. Plain Language Summary: The spatial co‐occurrence of drought and flood events has been receiving widespread attention in recent years since the superimposed condition of drought and flood has brought unprecedented challenges to disaster prevention, mitigation, and relief work. Eastern China is prone to droughts and floods, but their spatial interconnections are not yet well understood. Thus, we explore the historical evolution and the future change of simultaneous occurrence of droughts and floods over Eastern China under a warming climate. The spatial co‐occurrence of droughts and floods is projected to affect more land areas and become more frequent as climate warms. Eastern China experienced a pattern of more floods in Southeast China and more droughts in Northeast China during 1975–2004. We find that droughts tend to become more intense in Southeast China and the severity of floods is expected to increase in the northern region of East China, resulting in a shift pattern of more droughts in Southeast China and more floods in Northeast China. The shift pattern may be attributed to the weakening of western Pacific subtropical high strength and a northward displacement of the monsoon rain band in a changing climate. Key Points: Eastern China is projected to experience more frequent and more intense droughts and pluvials simultaneouslyThe spatial pattern of more pluvials in Southeast China and more droughts in Northeast China is expected to become an opposite pattern under a warming climateThe weakening strength of the western Pacific subtropical high and a northward displacement of the monsoon rain belt may contribute to the pattern of more pluvials in Northeast China and more droughts in Southeast China [ABSTRACT FROM AUTHOR]

The Qinling Mountains (QMs) are considered to be the division in geology, geochemistry, and physical geography between northern China and southern China. They have crucial effects on regional climate, especially on rainfall and temperature, and have shown great scientific relevance to climate change research in China. Using the observational daily and monthly rainfall and temperature data derived from meteorological and regional automatic stations—as well as the methods of correlation analysis, climate trend analysis, and Mann–Kendal and t tests—we revealed the spatiotemporal change characteristics of temperature and rainfall and their correlation with elevation, longitude, and latitude. The results show that the annual mean temperature (AMT) underwent a significant increasing trend in the QMs. The maximum AMT increase occurred in spring, and the minimum occurred in summer. Positive anomalies of annual mean rainfall amount (AMRA) occurred in the 1960s, 1980s, and 2010s, and negative anomalies occurred in the 1970s, 1990s, and 2000s. In the QMs, the amount of moderate rainfall (MR) occupied the maximum proportion and accounted for 27.9% of the AMRA, whereas the torrential rainfall (TR) occupied the minimum proportion and accounted for 12.8%. The AMRA amount significantly decreased by 130.1 mm from the 1980s to the 1990s and accounted for 13.5% of the measure in the 1980s. The AMT and AMRA showed consistent change trends with increases in elevation and latitude and showed the opposite trend as the longitude increased. The results offer a further understanding of the meteorological background of the QMs, helping us in further investigating the potential physical mechanisms that influence the spatiotemporal distribution characteristics of temperature and rainfall in the QMs. This study will provide a scientific basis for rainfall and temperature forecasts, with relevance to local ecosystems, agriculture, soil erosion, and the prevention and mitigation of floods in the future. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is an important meteorological component affecting the yield and protein content of winter wheat. The accuracy of climate model simulations of DTR will directly affect the prediction of winter wheat yield and quality. Previous model evaluations for worldwide or nationwide cannot answer which model is suitable for the estimation of winter wheat yield. We evaluated the ability of the coupled model intercomparison project phase 6 (CMIP6) models to simulate DTR in the winter wheat–growing regions of China using CN05 observations. The root mean square error (RMSE) and the interannual variability skill score (IVS) were used to quantitatively evaluate the ability of models in simulating DTR spatial and temporal characteristics, and the comprehensive rating index (CRI) was used to determine the most suitable climate model for winter wheat. The results showed that the CMIP6 model can reproduce DTR in winter wheat–growing regions. BCC-CSM2-MR simulations of DTR in the winter wheat–growing season were more consistent with observations. EC-Earth3-Veg simulated the climatological DTR best in the wheat growing regions (RMSE = 0.848). Meanwhile, the evaluation for climatological DTR in China is not applicable to the evaluation of DTR in winter wheat–growing regions, and the evaluation for annual DTR is not a substitute for the evaluation of winter wheat–growing season DTR. Our study highlights the importance of evaluating winter wheat–growing regions' DTR, which can further improve the ability of CMIP6 models simulating DTR to serve the research of climate change impact on winter wheat yield. [ABSTRACT FROM AUTHOR]

Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites. [ABSTRACT FROM AUTHOR]

Climate change has become one of the leading problems around the world. The transport sector is one of the major contributors to climate change. At the same time, climate change is also affecting transportation facilities and travel behaviour. This study proposed a bibliometric approach to explore the structure evolution development trends of this knowledge domain with a broader search scope and more objective results compared with a manual review. A total of 4073 peer-reviewed articles were collected from the WoS core collection database to conduct scientometric analysis. The collaboration analysis shows that the US, China, and European countries dominate this field, and international organisations' and government agencies' reports on climate change form the basis of this research field. A total of 14 co-citation clusters were identified, and the research on climate change and transportation primarily focused on the topics of policy options, travel behaviour, the COVID-19 lockdown, environmental cost, and air quality. Keyword co-occurrence evolution analysis was also conducted to track the latest research trends. Based on the results, we propose trends in four areas for future research. This study provides a holistic knowledge map for climate change and transportation research's past, present, and future. [ABSTRACT FROM AUTHOR]

Desertification is of significant concern as one of the world's most serious ecological and environmental problems. China has made great achievements in afforestation and desertification control in recent years. The climate varies greatly across northern China. Using a long-time series of remote sensing data to study the effects of desertification will further the understanding of China's desertification control engineering and climate change mechanisms. The moist index was employed in this research to determine the climate type and delineate the potential occurrence range of desertification in China. Then, based on the Google Earth Engine platform, MODIS data were used to construct various desertification monitoring indicators and applied to four machine learning models. By comparing different combinations of indicators and machine learning models, it was concluded that the random forest model with four indicator combinations had the highest accuracy of 86.94% and a Kappa coefficient of 0.84. Therefore, the random forest model with four indicator combinations was used to monitor desertification in the study area from 2000 to 2020. According to our studies, the area of desertification decreased by more than 237,844 km2 between 2000 and 2020 due to the impact of human activities and in addition to climatic factors such as the important role of precipitation. This research gives a database for the cause and control of desertification as well as a reference for national-scale desertification monitoring. [ABSTRACT FROM AUTHOR]

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3–7%) and by 13% (95% CI 10–16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research. [ABSTRACT FROM AUTHOR]

The evolution of the frequency of dry/wet spells is a hot topic in global climate change research. The synergistic effects of dry/wet spells make their consequences far more significant than a single disaster, having considerable impacts on hydrology, ecology, and economies, especially in the context of global warming. In this study, the standardized precipitation evapotranspiration index (SPEI)‐based identification of dry/wet spells was used to explore the evolution of the frequency of dry/wet abrupt alternation (DWAA) events in the past four decades in China, makes up for the lack of research on the evolution of such compound disasters. China has been affected by dry/wet spells in many areas for a long time. Studies found that high‐frequency DWAA areas are concentrated in densely populated areas, China's essential food bases and core areas for economic development. Dry‐to‐wet (D–W) events and wet‐to‐dry (W–D) events basically increased or decreased at the same pace in this period. We innovatively find DWAA events (D–W events and W–D events) are projected to occur increasingly in a wider region of China. The frequency of D–W events across China has increased from 2.35 in the 1980s to 3.56 in the last decade, a 50% increase. On the other hand, W–D events increased from 2.6 to 3.23, increasing 24%. The three regions with the most remarkable changes in the past four decades are Inner Mongolia, northwest China, and north China, where D–W events increased by 121, 61, and 58%, respectively, and W–D events increased by 71, 57, and 61%, respectively. Hereby, it is vital to implement effective prevention against DWAA events in the area which has been neglected in the past decades because of lower frequency. These results may help policymakers develop suitable disaster mitigation plans for reducing the potential risks of extreme compound natural events. [ABSTRACT FROM AUTHOR]

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade. [ABSTRACT FROM AUTHOR]

The three provinces of northeast China (NEPs) were one of the major grain producing areas in China and has undergone significant climate warming during past decades. However, nowadays researches on climate warming mostly focused on variations of mean temperature or temperature extremes, which missed much information in the other parts of temperature. Therefore, taken NEPs as study area, variations of maximum temperature (Tmax), minimum temperature (Tmin) and diurnal temperature range (DTR) at every 10% percentile from 1961 to 2019 in NEPs were analysed at seasonal scale. The abrupt changes and decadal shifts of Tmax, Tmin and DTR at different percentiles were further highlighted. Results show that (a) for either regional average or station cases, fluctuations of daily Tmax and daily Tmin both decreased in spring and winter, but increased in autumn. In summer, fluctuations of daily Tmin decreased while those of daily Tmax increased. (b) Spatially, Tmax heated up more in south of NEPs at lower percentiles, and in northwest of NEPs at higher percentiles in spring, summer and winter. Tmin and DTR overall showed consistent spatial differences that changes in north and northwest of NEPs were with greater magnitudes. (c) In spring and winter, the higher Tmax/Tmin had earlier abrupt changes than lower Tmax/Tmin, while in summer and autumn, the abrupt changes occurred earlier for lower Tmax/Tmin. (d) In winter, overwhelming positive shifts were significant before 2000, but turned negative after 2000. In summer, Tmax heated up quickly since 1990s and Tmin since 1980s. Shifts in spring and autumn varied during different periods and generally presented alternative warming at higher and lower percentiles. The research can help better depict the warming patterns and figure out the detailed warming procedures in NEPs, which is a strong supplement for current climate change research. [ABSTRACT FROM AUTHOR]

Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO2 (XCO2) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO2. We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO2 data. The full‐coverage and fine‐scale XCO2 data set is expected to advance our understanding of the carbon cycles. Plain Language Summary: As the most abundant greenhouse gas, atmospheric CO2 is considered one of the main contributors to climate change. Climate research requires comprehensive spatiotemporal distributions of CO2. However, the sparseness of the ground‐based measurements and lack of a fine‐scale modeled CO2 data set limits our understanding of the carbon dynamics. We developed a machine learning model to fill the data gaps in the satellite retrievals of the column‐averaged dry‐air mole fraction of CO2 (XCO2) for China during 2015–2018. The XCO2 levels were the highest in East China and the lowest in Northwest China, and were the lowest in summer and the highest in spring. While the XCO2 levels kept increasing, the rate of increase slightly declined. The full‐coverage and fine‐scale XCO2 data are valuable for policy making in carbon emission management. This work affirms the necessity of modeling the XCO2 from satellites, which can advance our understanding of the carbon cycles. Key Points: The machine learning model exhibited decent performance in predicting gridded daily XCO2, with cross‐validation R2 = 0.95The machine‐learning predicted XCO2 revealed considerably higher spatial heterogeneity than the CarbonTracker outputBased on the gap‐filled XCO2 data set, the average XCO2 during 2015–2018 was the highest in East China and the lowest in Northwest China [ABSTRACT FROM AUTHOR]

Black carbon (BC) aerosol measured at the WMO/GAW Station Mt. Waliguan from 1994 to 2017 has been analyzed. The 24 years long-term results showed that the average annual concentration ranges from 1.9 × 102 ng m−3 to 5.1 × 102 ng m−3 from 2001 to 2012, with a growth rate of 29%. However, the concentration of black carbon decreased from 2012 to 2016, with a decline rate of 64%. The monthly average concentration over the 24 years ranged from 90 ng m−3 to 7.0 × 102 ng m−3, with the peak value occurring in April and the lowest value occurring in November. The diurnal variation presented two peak types in different seasons, the first occurred at 20:00 a.m.~23:00 a.m. in the evening, and another around 06:00 a.m.~08:00 a.m. In addition, we found that the transport of black carbon aerosol is closely related to wind transport. The annual maximum black carbon concentration occurred in the east-northeast (ENE) wind direction, with a value of 4.6 × 102 ng m−3, and the second peak value occurred in the E wind direction, with a value of 3.9 × 102 ng m−3. The black carbon concentration of Waliguan was relatively high under the three wind directions of Northeast (NE), ENE, and east (E), which represented the influence of black carbon aerosol generated by human activities located on the east of the station. The 96-h backward trajectory analysis indicated that the sources in the southwest direction made a greater contribution to the black carbon concentration. the pollutants mainly came from the northwest and west sides according to the analysis of potential sources using the CWT approach. The study of black carbon evolution and contribution area is of great significance to further improve the capacity and level of global climate change research and prediction. [ABSTRACT FROM AUTHOR]

The influencing degree of different factors on terminal energy consumption of public building varies distinctly. If the actual measured terminal energy consumption is directly analyzed, the comparison basis is often inconsistent, which may lead to errors of results. In order to evaluate the energy consumption based on a unified standard, eliminating the interference of influencing factors, the standardization and revision of the measurement value of the terminal energy consumption is necessary. By considering domestic public building samples of different climate regions as research objects, this research has introduced a dimensionality reduction and integration method for evaluating influencing factors that could affect the terminal energy consumption of public buildings based on principal component and clustering analysis. Then, the quantitative mathematical expressions of the relationship between terminal energy consumptions and influencing factors were established through multiple linear regression fitting, formulating the standardized mathematical model of the terminal energy consumption of public buildings. According to the energy consumption standardized model, the evaluation approach was provided and scored based on mode analysis method. This approach can evaluate terminal energy consumption level and energy efficiency potential, analyzing the level of terminal energy consumption of public buildings in China. [ABSTRACT FROM AUTHOR]

China has been the world's largest emitter of carbon dioxide since 2006. To achieve the goal of carbon neutrality by 2060, social scientists must become involved in China's deep decarbonization process. However, Chinese social scientists have given little attention to climate change in their bibliometric research. Based on the Chinese Social Sciences Citation Index journal catalog of the four essential social sciences, namely, management, economics, politics, and sociology, we used the China National Knowledge Infrastructure database to quantify the extent to which Chinese social scientists are concerned about climate change. The results showed that from 1978 to 2020, 1179 articles on climate change were published in management, economics, politics, and sociology journals, which represented only 26.8% of the 4397 articles published on pollution in the same journals. Politics journals published the most articles (38.76%), while sociology journals published the fewest (2.37%). Thus, Chinese social scientists rarely considered climate change, mainly because this topic remains controversial in some fields and because of the influence of international politics in addition to the promotion and incentive mechanisms for researchers. We analyzed the keywords and evolution of climate change research in the four social sciences and our results show that social scientists should give greater emphasis to climate change in their research. [ABSTRACT FROM AUTHOR]

Soil moisture is a comprehensive reflection of soil moisture status and is an important parameter for land surface conditions. It is very important to study the distribution characteristics of soil moisture for ecological environment protection, scientific and rational utilization of soil water resources, and climate research. Using the soil layer humidity data sets of GLDAS-Noah v2.0 and v2.1, we analyzed the spatial-temporal distribution of soil moisture in China in a layer from 0 to 200 cm over 71 years from 1948 to 2018. Firstly, the Mann-Kendall trend test was used to analyze the trend of the changes and the spatial variation characteristics of soil moisture over the 71 years. Secondly, the coefficient of variation was used to analyze the temporal and spatial fluctuation of soil moisture in each layer of the study area over the 71 years. Finally, the Hurst index was used to predict the future trend of soil moisture changes in each layer. In addition, the correlation between soil moisture and the spatial-temporal variation of soil temperature in China was explored. The results show that the annual variation trend of soil moisture in the 0-200 cm soil layer has been consistent, that is, the soil humidity in most parts of east China has been decreasing, especially in northeast China, central China, the area surrounding the Yunnan Guizhou Plateau, and Taiwan Island, while it has been increasing in most of the western regions. Also, the change in soil layer humidity from 0 to 200 cm in southern China was greater than that in the northern region, and the humidity of the soil layer in the Pearl River Delta region was the most unstable. In addition, the spatial variation of soil moisture in the study area was relatively small from 1948 to 2001, but from 2002, the soil moisture throughout the study area became uneven. In the future, the trend of the change in soil moisture in most areas of China will remain consistent with that in the past 71 years, i.e., the soil in most parts of the east will gradually dry out and the soil moisture in most parts of the west will gradually increase; the soil humidity from 0 to 200 cm in most of the study area is inversely related to the soil temperature, and is mainly concentrated in northeast and central China, central and northern Inner Mongolia, the Qinghai Tibet Plateau, and Taiwan Island. [ABSTRACT FROM AUTHOR]

• We review the state of research on human-environment interaction in Holocene China. • We demonstrate the benefits of a multidisciplinary, regionally focused approach. • We conclude datasets must be local and specific, and integrate data on multiple scales. • China is large, well-documented, and critical for understanding social-natural systems. This paper reviews recent archaeological research on human-environment interaction in the Holocene, taking continental China as its geographic focus. As China is large, geographically diverse, and exceptionally archaeologically and historically well-documented, research here provides critical insight into the functioning of social-natural systems. Based on a broad review of the field as well as recent advances and discoveries, the authors reflect on research themes including climate change and adaptive systems theory, spatial and temporal scale, anthropogenic environmental change, risk management and resilience, and integration of subdisciplines. These converge on three overarching conclusions. First, datasets relevant to climate change and ancient human-environment interaction must be as local and specific as possible, as the timing of environmental change differs locally, and the human response is highly dependent on local social and technological conditions. Second, the field still needs more robust theoretical frameworks for analyzing complex social-natural systems, and especially for integrating data on multiple scales. Third, for this work to contribute meaningfully to contemporary climate change research, effective communication of research findings to the public and to scientists in other disciplines should be incorporated into publication plans. [ABSTRACT FROM AUTHOR]

A long-term Advanced Very High Resolution Radiometer (AVHRR) snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER AVHRR SCE product has a spatial resolution of 5 km and a daily temporal resolution, and it is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination, and gap-filling (GF). A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer's accuracy was 81.0 %, the user's accuracy was 81.3 %, and the Cohen's kappa (CK) value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 87.3 %, a producer's accuracy of 86.7 %, a user's accuracy of 95.7 %, and a Cohen's kappa value of 0.695. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 %, the omission error dropped from 60.8 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is already available at 10.11888/Snow.tpdc.271381 (Hao et al., 2021). [ABSTRACT FROM AUTHOR]