Importance of soil moisture conservation in mitigating climate change.

A troubling feedback loop, where drier soil contributes to hotter climates, has been widely recognized. This study, drawing on climate model simulations, reveals that maintaining current global soil moisture levels could significantly alleviate 32.9% of land warming under low-emission scenarios. This action could also postpone reaching critical warming thresholds of 1.5 °C and 2.0 °C by at least a decade. Crucially, preserving soil moisture at current levels could prevent noticeable climate change impacts across 42% of the Earth's land, a stark deviation from projections suggesting widespread impacts before the 2060s. To combat soil drying, afforestation in mid-to-low latitude regions within the next three decades is proposed as an effective strategy to increase surface water availability. This underscores the substantial potential of nature-based solutions for managing soil moisture, benefiting both climate change mitigation and ecological enhancement.

Unveiling the Molecular Characteristics, Origins, and Formation Mechanism of Reduced Nitrogen Organic Compounds in the Urban Atmosphere of Shanghai Using a Versatile Aerosol Concentration Enrichment System.

Reduced nitrogen-containing organic compounds (NOCs) in aerosols play a crucial role in altering their light-absorption properties, thereby impacting regional haze and climate. Due to the low concentration levels of individual NOCs in the air, the utilization of accurate detection and quantification technologies becomes essential. For the first time, this study investigated the diurnal variation, chemical characteristics, and potential formation pathways of NOCs in urban ambient aerosols in Shanghai using a versatile aerosol concentration enrichment system (VACES) coupled with HPLC-Q-TOF-MS. The results showed that NOCs accounted over 60% of identified components of urban organic aerosols, with O/N < 3 compounds being the major contributors (>70%). The predominance of the positive ionization mode suggested the prevalence of reduced NOCs. Higher relative intensities and number fractions of NOCs were observed during nighttime, while CHO compounds showed an opposite trend. Notably, a positive correlation between the intensity of NOCs and ammonium during the nighttime was observed, suggesting that the reaction of ammonium to form imines may be a potential pathway for the formation of reduced NOCs during the nighttime. Seven prevalent types of reduced NOCs in autumn and winter were identified and characterized by an enrichment of CH2 long-chain homologues. These NOCs included alkyl, cyclic, and aromatic amides in CHON compounds, as well as heterocyclic or cyclic amines and aniline homologue series in CHN compounds, which were associated with anthropogenic activities and may be capable of forming light-absorbing chromophores or posing harm to human health. The findings highlight the significant contributions of both primary emissions and ammonium chemistry, particularly amination processes, to the pollution of reduced NOCs in Shanghai's atmosphere.

Quantification and Characterization of Fine Plastic Particles as Considerable Components in Atmospheric Fine Particles.

The negative effects of air pollution, especially fine particulate matter (PM2.5, particles with an aerodynamic diameter of ≤2.5 µm), on human health, climate, and ecosystems are causing significant concern. Nevertheless, little is known about the contributions of emerging pollutants such as plastic particles to PM2.5 due to the lack of continuous measurements and characterization methods for atmospheric plastic particles. Here, we investigated the levels of fine plastic particles (FPPs) in PM2.5 collected in urban Shanghai at a 2 h resolution by using a novel versatile aerosol concentration enrichment system that concentrates ambient aerosols up to 10-fold. The FPPs were analyzed offline using the combination of spectroscopic and microscopic techniques that distinguished FPPs from other carbon-containing particles. The average FPP concentrations of 5.6 µg/m3 were observed, and the ratio of FPPs to PM2.5 was 13.2% in this study. The FPP sources were closely related to anthropogenic activities, which pose a potential threat to ecosystems and human health. Given the dramatic increase in plastic production over the past 70 years, this study calls for better quantification and control of FPP pollution in the atmosphere.

Soil moisture-atmosphere coupling accelerates global warming.

Soil moisture-atmosphere coupling (SA) amplifies greenhouse gas-driven global warming via changes in surface heat balance. The Scenario Model Intercomparison Project projects an acceleration in SA-driven warming due to the 'warmer climate - drier soil' feedback, which continuously warms the globe and thereby exerts an acceleration effect on global warming. The projection shows that SA-driven warming exceeds 0.5 °C over extratropical landmasses by the end of the 21st Century. The likelihood of extreme high temperatures will additionally increase by about 10% over the entire globe (excluding Antarctica) and more than 30% over large parts of North America and Europe under the high-emission scenario. This demonstrates the high sensitivity of SA to climate change, in which SA can exceed the natural range of climate variability and play a non-linear warming component role on the globe.

Accelerating the energy transition towards photovoltaic and wind in China.

China's goal to achieve carbon (C) neutrality by 2060 requires scaling up photovoltaic (PV) and wind power from 1 to 10-15 PWh year-1 (refs. 1-5). Following the historical rates of renewable installation1, a recent high-resolution energy-system model6 and forecasts based on China's 14th Five-year Energy Development (CFED)7, however, only indicate that the capacity will reach 5-9.5 PWh year-1 by 2060. Here we show that, by individually optimizing the deployment of 3,844 new utility-scale PV and wind power plants coordinated with ultra-high-voltage (UHV) transmission and energy storage and accounting for power-load flexibility and learning dynamics, the capacity of PV and wind power can be increased from 9 PWh year-1 (corresponding to the CFED path) to 15 PWh year-1, accompanied by a reduction in the average abatement cost from US$97 to US$6 per tonne of carbon dioxide (tCO2). To achieve this, annualized investment in PV and wind power should ramp up from US$77 billion in 2020 (current level) to US$127 billion in the 2020s and further to US$426 billion year-1 in the 2050s. The large-scale deployment of PV and wind power increases income for residents in the poorest regions as co-benefits. Our results highlight the importance of upgrading power systems by building energy storage, expanding transmission capacity and adjusting power load at the demand side to reduce the economic cost of deploying PV and wind power to achieve carbon neutrality in China.

Recent Advances in Understanding Multi-scale Climate Variability of the Asian Monsoon.

Studies of the multi-scale climate variability of the Asian monsoon are essential to an advanced understanding of the physical processes of the global climate system. In this paper, the progress achieved in this field is systematically reviewed, with a focus on the past several years. The achievements are summarized into the following topics: (1) the onset of the South China Sea summer monsoon; (2) the East Asian summer monsoon; (3) the East Asian winter monsoon; and (4) the Indian summer monsoon. Specifically, new results are highlighted, including the advanced or delayed local monsoon onset tending to be synchronized over the Arabian Sea, Bay of Bengal, Indochina Peninsula, and South China Sea; the basic features of the record-breaking mei-yu in 2020, which have been extensively investigated with an emphasis on the role of multi-scale processes; the recovery of the East Asian winter monsoon intensity after the early 2000s in the presence of continuing greenhouse gas emissions, which is believed to have been dominated by internal climate variability (mostly the Arctic Oscillation); and the accelerated warming over South Asia, which exceeded the tropical Indian Ocean warming, is considered to be the main driver of the Indian summer monsoon rainfall recovery since 1999. A brief summary is provided in the final section along with some further discussion on future research directions regarding our understanding of the Asian monsoon variability.

Integrative transcriptome and metabolome analysis reveals the mechanism of exogenous melatonin alleviating drought stress in maize roots.

Melatonin (MT) is essential for plant development and drought adaptation. However, the molecular and metabolic mechanisms underlying MT-induced drought tolerance in maize roots remain largely unclear. Herein, we investigated the effects of MT on drought tolerance in maize roots using integrated transcriptomic and metabolomic analyses, and identified MT-induced genes and metabolites associated with drought resistance. Compared with the untreated control plants, MT application alleviated the deleterious effects of drought on roots, by decreasing the malondialdehyde level and increasing the solute potential, eventually promoting root growth. Transcriptome and metabolome analysis demonstrated that MT significantly upregulates the expression of genes related to flavonoid biosynthesis (PAL, C4H, 4CL, HCT, CHS, CHI, F3'5'H, and DFR), activates drought-responsive transcription factors (ERFs, NACs, MYBs, and bHLHs), and regulates hormone signaling-related genes, especially ethylene response factors (ERF4, ERF81, and ERF110). Moreover, MT increased the accumulation of flavonoid metabolites, particularly apigenin, luteolin, and quercetin, under drought-stress conditions. These findings were further supported by quantitative real-time polymerase chain reaction analysis and total flavonoid measurements. Altogether, our findings suggest that MT promotes maize root growth during drought by regulating flavonoid synthesis pathways, transcription factors, and plant hormone signals. This study provides new insights into the complex mechanisms by which MT enhances crop resistance to drought damage.

Delayed use of bioenergy crops might threaten climate and food security.

The potential of mitigation actions to limit global warming within 2 °C (ref. 1) might rely on the abundant supply of biomass for large-scale bioenergy with carbon capture and storage (BECCS) that is assumed to scale up markedly in the future2-5. However, the detrimental effects of climate change on crop yields may reduce the capacity of BECCS and threaten food security6-8, thus creating an unrecognized positive feedback loop on global warming. We quantified the strength of this feedback by implementing the responses of crop yields to increases in growing-season temperature, atmospheric CO2 concentration and intensity of nitrogen (N) fertilization in a compact Earth system model9. Exceeding a threshold of climate change would cause transformative changes in social-ecological systems by jeopardizing climate stability and threatening food security. If global mitigation alongside large-scale BECCS is delayed to 2060 when global warming exceeds about 2.5 °C, then the yields of agricultural residues for BECCS would be too low to meet the Paris goal of 2 °C by 2200. This risk of failure is amplified by the sustained demand for food, leading to an expansion of cropland or intensification of N fertilization to compensate for climate-induced yield losses. Our findings thereby reinforce the urgency of early mitigation, preferably by 2040, to avoid irreversible climate change and serious food crises unless other negative-emission technologies become available in the near future to compensate for the reduced capacity of BECCS.

The Effects of Short-Term PM2.5 Exposure on Pulmonary Function among Children with Asthma-A Panel Study in Shanghai, China.

Fine particulate matter (PM2.5) has been reported to be an important risk factor for asthma. This study was designed to evaluate the relationship between PM2.5 and lung function among children with asthma in Shanghai, China. From 2016 to 2019, a total of 70 Chinese children aged 4 to 14 in Shanghai were recruited for this panel study. The questionnaire was used to collect baseline information, and the lung function covering forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and peak expiratory flow (PEF) were carried out for each child more than twice during follow-up. Meanwhile, the simultaneous daily air atmospheric pollutants and meteorological data were collected. The linear mixed effect (LME) model was used to assess the relationship between air pollutants and lung function. A significantly negative association was found between PM2.5 and lung function in children with asthma. In the single-pollutant model, the largest effects of PM2.5 on lung function were found for lag 0-2, with FVC and FEV1 decreasing by 0.91% [95% confidence interval (CI): -1.75, -0.07] and 1.05% (95% CI: -2.09, 0.00), respectively, for each 10 µg/m3 increase in PM2.5. In the multi-pollution model (adjusted PM2.5 + SO2 + O3), the maximum effects of PM2.5 on FVC and FEV1 also appeared for lag 0-2, with FVC and FEV1 decreasing by 1.57% (95% CI: -2.69, -0.44) and 1.67% (95% CI: -3.05, -0.26), respectively, for each 10 µg/m3 increase in PM2.5. In the subgroup analysis, boys, preschoolers (<6 years old) and hot seasons (May to September) were more sensitive to changes. Our findings may contribute to a better understanding of the short-term exposure effects of PM2.5 on lung function in children with asthma.

Transcriptomic and physiological responses of contrasting maize genotypes to drought stress.

Drought is a significant environmental stress factor that adversely affects maize productivity. However, many details regarding the molecular mechanisms of maize against drought are still unclear. In this study, leaf transcriptomics and physiological traits of two maize genotypes with differing drought resistance were analyzed. Transcriptome sequencing identified 8985 and 7305 differentially expressed genes (DEGs) in SD902 and SD609, respectively. Functional analysis suggested that numerous genes are highly involved in oxidative defense, protein modification, photosynthesis, phytohormone response, MAPK signaling, and transcription factors (TFs). Compared to SD902, SD609 had a higher expression of DEGs related to antioxidant enzymes, photosynthetic electron transport, heat shock proteins, and indole-3-acetic acid (IAA) signaling under drought conditions, which might contribute to its tolerance mechanisms to drought. Stress-induced TFs may play a crucial regulatory role in genotypic differences. Moreover, the physiological changes and gene expression abundance determined using quantitative reverse transcription polymerase chain reaction were consistent with the RNA sequencing data. The study results suggest that the higher drought tolerance of SD609 than SD902 can be attributed to stronger stress defense capabilities, IAA signal transduction, and more stable photosynthesis. Our findings provide new insights into the molecular mechanisms of maize against drought stress, and the candidate genes identified may be used in breeding drought-tolerant maize cultivars.

MERRA-2 PM2.5 mass concentration reconstruction in China mainland based on LightGBM machine learning.

MERRA-2 developed by the National Aeronautics and Space Administration (NASA) provides the long-term record of surface PM2.5 mass concentration since 1980s, but needs great improvement over mainland China according to recent studies. In this study, a newly developed light gradient boosting machine (LGBM) model is introduced to correct the MERRA-2 PM2.5 record over mainland China by incorporating the meteorological reanalysis and satellite AOD retrievals. A 40-year surface PM2.5 record covering mainland China is reconstructed from 1980 to 2019, providing a new dataset for exploring the interactions between climate variability and air pollution. The new record exhibits not only much better magnitude but also more excellent variabilities of surface PM2.5 loading compared to original MERRA-2 products. The correlation coefficient, the root-mean-square error and the mean error between the observed and reconstructed records are 0.8, less than 28.5 µg·m-3, and 0.33 µg·m-3, respectively, which are much better than those of 0.27, 45.8 µg·m-3, and 1.64 µg·m-3 between the observed and MERRA-2 PM2.5 records. The PM2.5 record with longer term and higher accuracy developed in this study provides a better base for the research on the climate change variability and air pollution in mainland China. However, limitations of the reconstructed record still exist, especially in the Tibetan Plateau and marine regions with very sparse surface measurements, which need further correction in the future studies.

From concept to action: a united, holistic and One Health approach to respond to the climate change crisis.

It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.

Melatonin Enhances Drought Tolerance by Regulating Leaf Stomatal Behavior, Carbon and Nitrogen Metabolism, and Related Gene Expression in Maize Plants.

It is commonly known that exogenously applied melatonin can alleviate the impact of drought stress, but the mechanism used by melatonin to regulate stomatal behavior and carbon (C) and nitrogen (N) metabolism to increase drought resistance remains elusive. Herein, our aim was to investigate the influence of exogenous melatonin on the regulation of C and N metabolism in maize plants under water deficit. In this study, we analyzed stomatal behavior, the key components of C and N metabolism, and the gene expression and activity of enzymes involved in the C and N metabolism in maize plants. The results showed that the application of melatonin (100 µM) significantly increased maize growth and sustained the opening of stomata, and secondarily increased the photosynthetic capacity in maize. Under drought stress, foliar application of melatonin induced the gene transcription and activities of sucrose phosphate synthetase, ADP-glucose pyrophosphorylase, phosphoenolpyruvate carboxylase, and citrate synthase, resulting in the enhancement of sucrose and starch synthesis and the tricarboxylic acid (TCA) cycle. This enhancement in sugar biosynthesis and the TCA cycle might lead to stronger N assimilation. As anticipated, NO3 - reduction and NH4 + assimilation were also strengthened after melatonin treatment under drought stress. An increase was observed in some key enzymatic activities and transcription involved in nitrogen metabolism, such as that of nitrate reductase, nitrite reductase, glutamate synthase, and glutamine synthetase, in melatonin-treated, drought-stressed maize. Moreover, melatonin attenuated the drought-induced damage by reducing protein degradation and increasing the level of proline. Conclusively, our results indicate that exogenous melatonin enhances drought tolerance in maize via promoting stomatal opening and regulating C and N metabolism and related gene expression.

Physiological and proteomic analyses revealed the response mechanisms of two different drought-resistant maize varieties.

BACKGROUND: Drought stress severely limits maize seedling growth and crop yield. Previous studies have elucidated the mechanisms by which maize acquires drought resistance and contends with water deficiency. However, the link between the physiological and molecular variations among maize cultivars are unknown. Here, physiological and proteomic analyses were conducted to compare the stress responses of two maize cultivars with contrasting drought stress tolerance. RESULTS: The physiological analysis showed that the drought-tolerant SD609 maize variety maintains relatively high photochemical efficiency by enhancing its protective cyclic electron flow (CEF) mechanism and antioxidative enzymes activities. Proteomics analysis revealed that 198 and 102 proteins were differentially expressed in SD609 and the drought-sensitive SD902 cultivar, respectively. GO and KEGG enrichments indicated that SD609 upregulated proteins associated with photosynthesis, antioxidants/detoxifying enzymes, molecular chaperones and metabolic enzymes. Upregulation of the proteins related to PSII repair and photoprotection improved photochemical capacity in SD609 subjected to moderate drought stress. In SD902, however, only the molecular chaperones and sucrose synthesis pathways were induced and they failed to protect the impaired photosystem. Further analysis demonstrated that proteins related to the electron transport chain (ETC) and redox homeostasis as well as heat shock proteins (HSPs) may be important in protecting plants from drought stress. CONCLUSIONS: Our experiments explored the mechanism of drought tolerance and clarified the interconnections between the physiological and proteomic factors contributing to it. In summary, our findings aid in further understanding of the drought tolerance mechanisms in maize.

Physiological and transcriptomic analyses of the effects of exogenous melatonin on drought tolerance in maize (Zea mays L.).

Water deficit inhibits maize (Zea mays L.) seedling growth and yield. Application of exogenous melatonin can improve drought tolerance of corn, but little is known regarding the transcriptional mechanisms of melatonin-mediated drought tolerance in maize. Increased understanding of the effects of melatonin on maize plants under drought stress is vital to alleviate the adverse effects of drought on food production in the future. The aim of this investigation was to use physiological and transcriptome analyses for exploring the possible mechanisms of exogenous melatonin against drought stress in maize. In this study, maize seedlings were subjected to drought stress and some were treated with exogenous melatonin. The physiological results showed that melatonin inhibited H2O2 accumulation and promoted the scavenging of excessive reactive oxygen species to reduce oxidative damage in maize leaves. Transcriptomic analysis identified 957 differentially expressed genes between melatonin and non-melatonin treatment groups. Further detailed analyses suggested that melatonin-regulated genes are mainly related to glutathione metabolism, calcium signaling transduction, and jasmonic acid biosynthesis. Some transcription factor families, such as WRKY, AP2/ERF-ERF, MYB, NAC, and bZIP, were also activated by exogenous melatonin. Moreover, crosstalk between melatonin and other hormones that mediate drought tolerance was observed. In conclusion, the combination of physiological and transcriptome analyses revealed some mechanisms underlying the role of melatonin in alleviating drought; knowledge of these mechanisms may assist in successful maize cultivation under drought stress.

El Niño Modoki can be mostly predicted more than 10 years ahead of time.

The 2014-2015 "Monster"/"Super" El Niño failed to be predicted one year earlier due to the growing importance of a new type of El Niño, El Niño Modoki, which reportedly has much lower forecast skill with the classical models. In this study, we show that, so far as of today, this new El Niño actually can be mostly predicted at a lead time of more than 10 years. This is achieved through tracing the predictability source with an information flow-based causality analysis, which has been rigorously established from first principles during the past 16 years (e.g., Liang in Phys Rev E 94:052201, 2016). We show that the information flowing from the solar activity 45 years ago to the sea surface temperature results in a causal structure resembling the El Niño Modoki mode. Based on this, a multidimensional system is constructed out of the sunspot number series with time delays of 22-50 years. The first 25 principal components are then taken as the predictors to fulfill the prediction, which through causal AI based on the Liang-Kleeman information flow reproduces rather accurately the events thus far 12 years in advance.

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Mode separation with one hydrophone in shallow water: A sparse Bayesian learning approach based on phase speed.

An approach of broadband mode separation in shallow water is proposed using phase speed extracted from one hydrophone and solved with sparse Bayesian learning (SBL). The approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, is used to build the dictionary matrix for SBL. Given a multi-frequency pressure vector on one hydrophone, SBL estimates a set of sparse coefficients for a large number of atoms in the dictionary. With the estimated coefficients and corresponding atoms, the separated normal modes are retrieved. The presented method can be used for impulsive or known-form signals in a shallow-water environment while no bottom information is required. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario.

Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions.

Heavy monsoon rainfall ravaged a large swath of East Asia in summer 2020. Severe flooding of the Yangtze River displaced millions of residents in the midst of a historic public health crisis. This extreme rainy season was not anticipated from El Niño conditions. Using observations and model experiments, we show that the record strong Indian Ocean Dipole event in 2019 is an important contributor to the extreme Yangtze flooding of 2020. This Indian Ocean mode and a weak El Niño in the Pacific excite downwelling oceanic Rossby waves that propagate slowly westward south of the equator. At a mooring in the Southwest Indian Ocean, the thermocline deepens by a record 70 m in late 2019. The deepened thermocline helps sustain the Indian Ocean warming through the 2020 summer. The Indian Ocean warming forces an anomalous anticyclone in the lower troposphere over the Indo-Northwest Pacific region and intensifies the upper-level westerly jet over East Asia, leading to heavy summer rainfall in the Yangtze Basin. These coupled ocean-atmosphere processes beyond the equatorial Pacific provide predictability. Indeed, dynamic models initialized with observed ocean state predicted the heavy summer rainfall in the Yangtze Basin as early as April 2020.