The South Atlantic–South Indian Ocean Pattern: a Zonally Oriented Teleconnection along the Southern Hemisphere Westerly Jet in Austral Summer

Extratropical teleconnections significantly affect the climate in subtropical and mid-latitude regions. Understanding the variability of atmospheric teleconnection in the Southern Hemisphere, however, is still limited in contrast with the well-documented counterpart in the Northern Hemisphere. This study investigates the interannual variability of mid-latitude circulation in the Southern Hemisphere in austral summer based on the ERA-Interim reanalysis dataset during 1980−2016. A stationary mid-latitude teleconnection is revealed along the strong Southern Hemisphere westerly jet over the South Atlantic and South Indian Ocean (SAIO). The zonally oriented SAIO pattern represents the first EOF mode of interannual variability of meridional winds at 200 hPa over the region, with a vertical barotropic structure and a zonal wavenumber of 4. It significantly modulates interannual climate variations in the subtropical Southern Hemisphere in austral summer, especially the opposite change in rainfall and surface air temperature between Northwest and Southeast Australia. The SAIO pattern can be efficiently triggered by divergences over mid-latitude South America and the southwest South Atlantic, near the entrance of the westerly jet, which is probably related to the zonal shift of the South Atlantic Convergence Zone. The triggered wave train is then trapped within the Southern Hemisphere westerly jet waveguide and propagates eastward until it diverts northeastward towards Australia at the jet exit, in addition to portion of which curving equatorward at approximately 50° E towards the southwest Indian Ocean

Simulated Impact of the Tibetan Glacier Expansion on the Eurasian Climate and Glacial Surface Mass Balance during the Last Glacial Maximum

Glaciers over the Tibetan Plateau and surrounding regions during the Last Glacial Maximum (LGM) were much more extensive than during the preindustrial period (PI). The climate impact of such glacial expansion is studied here using the Community Atmosphere Model, version 4 (CAM4). To cover the range of uncertainty in glacier area during the LGM, the following three values are tested: 0.35 x 10(6), 0.53 x 10(6), and 0.70 x 10(6) km(2). The added glacier is distributed approximately equally over the Pamir region and the Himalayas. If 0.70 x 10(6) km(2) is used, the annual mean surface temperature of the glaciated regions would be cooled by similar to 3.5 degrees C. The annual mean precipitation would be reduced by 0.2 mm day(-1) (10%) and 2.5 mm day(-1) (24%) over the Pamir region and Himalayas, respectively. The surface mass balance (SMB) of the glaciers changes by 0.55 m yr(-1) (280%) and 20.32 m yr(-1) (220%) over the two regions, respectively. The changes in SMB remain large (0.29 and 20.13 m yr(-1)), even if the area of the Tibetan glacier were 0.35x10(6) km(2). Therefore, based on the results of this particular model, the expansion of glaciers can either enhance or slow the glacial growth. Moreover, the expansion of glaciers over the Himalayas reduces summer precipitation in central and northern China by similar to 0.5 mm day(-1) and increases summer precipitation in southern Asia by similar to 0.6 mm day(-1). The expansion of glaciers over the Pamir region has a negligible influence on the precipitation in these monsoonal regions, which is likely due to its large distance from the main monsoonal regions

Climate influence on the 2019 fires in Amazonia

Amazonia experienced unusually devastating fires in August 2019, leading to huge regional and global environmental and economic losses. The increase in fires has been largely attributed to anthropogenic deforestation, but anomalous climate conditions could also have contributed. This study investigates the climate influence on Amazonia fires in August 2019 and underlying mechanisms, based on statistical correlation and multiple linear regression analyses of 2001-2019 satellite-based fire products and multiple observational or reanalyzed climate datasets. Positive fire anomalies in August 2019 were mainly located in southern Amazonia. These anomalies were mainly driven by low precipitation and relative humidity, which increased fuel dryness and contributed to 38.9 +/- 9.5% of the 2019 anomaly in pyrogenic carbon emissions over the southern Amazonia. The dry conditions were associated with southerly wind anomalies over southern Amazonia that suppressed the climatological southward transport of water vapor originating from the Atlantic. The southerly wind anomalies were caused by the combination of a Gill-type cyclonic response to the warmer North Atlantic sea surface temperature (SST), and enhancement of the Walker and Hadley circulations over South America due to the colder SST in the eastern Pacific, and a mid-latitude wave train triggered by the warmer condition in the western Indian Ocean. Our study highlights, for the first time, the important role of Indian Ocean SST for fires in Amazonia. It also reveals how cold SST anomalies in the tropical eastern Pacific link the warm phase of the El Nino-Southern Oscillation (ENSO) in the preceding December-January to the dry-season fires in Amazonia. Our findings can develop theoretical basis of global tropical SST-based fire prediction, and have potential to improve prediction skill of extreme fires in Amazonia and thus to take steps to mitigate their impacts which is urgency given that dry conditions led to the extreme fires are becoming common in Amazonia. (c) 2021 Elsevier B.V. All rights reserved.11Nsciescopu

Climate influence on the 2019 fires in Amazonia

Amazonia experienced unusually devastating fires in August 2019, leading to huge regional and global environmental and economic losses. The increase in fires has been largely attributed to anthropogenic deforestation, but anomalous climate conditions could also have contributed. This study investigates the climate influence on Amazonia fires in August 2019 and underlying mechanisms, based on statistical correlation and multiple linear regression analyses of 2001–2019 satellite-based fire products and multiple observational or reanalyzed climate datasets. Positive fire anomalies in August 2019 were mainly located in southern Amazonia. These anomalies were mainly driven by low precipitation and relative humidity, which increased fuel dryness and contributed to 38.9 ± 9.5% of the 2019 anomaly in pyrogenic carbon emissions over the southern Amazonia. The dry conditions were associated with southerly wind anomalies over southern Amazonia that suppressed the climatological southward transport of water vapor originating from the Atlantic. The southerly wind anomalies were caused by the combination of a Gill-type cyclonic response to the warmer North Atlantic sea surface temperature (SST), and enhancement of the Walker and Hadley circulations over South America due to the colder SST in the eastern Pacific, and a mid-latitude wave train triggered by the warmer condition in the western Indian Ocean. Our study highlights, for the first time, the important role of Indian Ocean SST for fires in Amazonia. It also reveals how cold SST anomalies in the tropical eastern Pacific link the warm phase of the El Niño-Southern Oscillation (ENSO) in the preceding December–January to the dry-season fires in Amazonia. Our findings can develop theoretical basis of global tropical SST-based fire prediction, and have potential to improve prediction skill of extreme fires in Amazonia and thus to take steps to mitigate their impacts which is urgency given that dry conditions led to the extreme fires are becoming common in Amazonia

Summer Onset in Northern East Asia: Feature, Mechanism and Variability

Summer in the East Asian monsoon region is characterized by heavy rainfall and high temperature. Its onset, depicted by monsoon rainfall and/or airflow as well as surface air temperature, has been well documented. However, the onset of summer season is rarely addressed in northern East Asia (NEA) around the northern edge of the East Asian summer monsoon. This study investigates the feature, mechanism, and variability of the summer onset in NEA based on the ERA-5 reanalysis dataset for 1979–2020. Results show that, in climatology, the onset of summer in NEA occurs in pentad 31 when the spring-to-summer warming process is decelerated at the highest rate. The change in the warming rate is mainly attributed to a decrease in the diabatic heat, mostly surface sensible heat, and temperature advection plays a small role. After the onset of summer, regional low-level northwesterly winds are weakened, and a local NEA cyclonic low forms. The latter, coupled with monsoon southerly airflow to the south, advects more moisture into NEA and increases regional rainfall. Furthermore, a temperature threshold of 17 °C, the climatological regional mean surface air temperature in pentad 31, was proposed to depict summer onset in NEA. Based on the temperature threshold, the year-to-year variability of summer onset timing in NEA is revealed, ranging from pentad 29 (late May) to 34 (middle June), with the standard deviation of 1.2 pentads. It advanced by 0.6 pentads, on average, after the late 1990s. This study provides a new method to objectively quantify the timing of summer onset in East Asia, which is thermodynamically explainable and may help us to depict and monitor summer onset in different latitudes and topography

Ion Fusion of High-Resolution LC MS-Based Metabolomics Data to Discover More Reliable Biomarkers

A systematic approach for the fusion of associated ions from a common molecule was developed to generate "one feature for one peak" metabolomics data. This approach guarantees that each molecule is equally selected as a potential biomarker and may largely enhance the chance to obtain reliable findings without employing redundant ion information. The ion fusion is based on low mass variation in contrast to the theoretical calculation measured by a high-resolution mass spectrometer, such as LTQ orbitrap, and a high correlation of ion pairs from the same molecule. The mass characteristics of isotopic distribution, neutral loss, and adduct ions were simultaneously applied to inspect each extracted ion in the range of a predefined retention time window. The correlation coefficient was computed with the corresponding intensities of each ion pair among all experimental samples. Serum metabolomics data for the investigation of hepatocellular carcinoma (HCC) and healthy controls were utilized as an example to demonstrate this strategy. In total, 609 and 1084 ion pairs were respectively found meeting one or more criteria for fusion, and therefore fused to 106 and 169 metabolite features of the datasets in the positive and negative modes, respectively. The important metabolite features were separately discovered and compared to distinguish the HCC from the healthy controls using the two datasets with and without ion fusion. The results show that the developed method can be an effective tool to process high-resolution mass spectrometry data in "omics" studies

Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study

In metabolomics research, false positive features from non-sample sources and noises usually exist in the peak table, they will make the results of screening differential metabolites or biomarkers unreliable. In this study, a method to remove false positive features (rFPF) was developed to improve the quality of the peak table. rFPF recognizes real peak profiles based on the information entropy and statistical correlation, and eliminates false positive features from non-sample sources and noises. A standard mixture with 42 standards (14 isotopic labeled internal standards and 28 common standards) and a urine sample were applied to evaluate the effectiveness of the rFPF method. The analysis results of metabolite standards showed that more than 92% false positive features were removed by rFPF, but target standards completely remained. The analysis results of urine sample showed that the number of features was significantly reduced from 7182 to 2522. Interestingly, 98% of the identified metabolites remained after removing false positive features. The proposed rFPF shows great prospects as a new data handling method for metabolomics studies. (C) 2019 Elsevier B.V. All rights reserved

Screening out Biomarkers of <i>Tetrastigma hemsleyanum</i> for Anti-Cancer and Anti-Inflammatory Based on Spectrum-Effect Relationship Coupled with UPLC-Q-TOF-MS

Tetrastigma hemsleyanum Diels et Gilg. (T. hemsleyanum) is an economically and medicinally valuable species within the genus Tetrastigma. However, the material basis of its pharmacological action and the biomarkers associated with its anti-cancer and anti-inflammatory effects are still unclear. Additionally, the T. hemsleyanum industry cannot grow because there is a lack of a scientific, universal, and measurable quality control system. This study aimed to explore the chemical basis quality markers related to the anti-cancer and anti-inflammatory effects of T. hemsleyanum to establish an effective quality evaluation method. UPLC-Q-TOF-MSE fingerprint profiles of T. hemsleyanum from different origins were established. Pharmacodynamic studies used HepG2 and HuH-7 cells and LPS-induced RAW264.7 to evaluate the anti-tumor and anti-inflammatory effects of the active ingredients. The spectrum-effect relationships between UPLC fingerprints and anti-cancer and anti-inflammatory activities were evaluated using PCA and PLSR statistical methods. Moreover, docking analysis was performed to identify specific active biomarkers with molecular targets associated with cancer and inflammation. Chlorogenic acid, quinic acid, catechin, kaempferol 3-rutinoside, apigenin-8-C-glucoside, and linolenic acid were associated with anticancer activity, while chlorogenic acid, quercetin, quinic acid, kaempferol 3-rutinoside, rutinum, apigenin-8-C-glucoside, and linolenic acid were associated with anti-inflammatory activity. The spectrum-effect relationship of T. hemsleyanum was successfully established, and the biomarkers for anti-cancer and anti-inflammatory effects were preliminary confirmed. These findings provide a theoretical basis for the elucidation of the substance basis of T. hemsleyanum and lay the foundation for its rapid identification, quality control, industrial research, and utilization

A Novel Strategy for Large-Scale Metabolomics Study by Calibrating Gross and Systematic Errors in Gas Chromatography-Mass Spectrometry

Metabolomics is increasingly applied to discover and validate metabolite biomarkers and illuminate biological variations. Combination of multiple analytical batches in large-scale and long-term metabolomics is commonly utilized to generate robust metabolomics data, but gross and systematic errors are often observed. The appropriate calibration methods are required before statistical analyses. Here, we develop a novel correction strategy for large-scale and long-term metabolomics study, which could integrate metabolomics data from multiple batches and different instruments by calibrating gross and systematic errors. The gross error calibration method applied various statistical and fitting models of the feature ratios between two adjacent quality control (QC) samples to screen and calibrate outlier variables. Virtual QC of each sample was produced by a linear fitting model of the feature intensities between two neighboring QCs to obtain a correction factor and remove the systematic bias. The suggested method was applied to handle metabolic profiling data of 1197 plant samples in nine batches analyzed by two gas chromatography mass spectrometry instruments. The method was evaluated by the relative standard deviations of all the detected peaks, the average Pearson correlation coefficients, and Euclidean distance of QCs and non-QC replicates. The results showed the established approach outperforms the commonly used internal standard correction and total intensity signal correction methods, it could be used to integrate the metabolomics data from multiple analytical batches and instruments, and it allows the frequency of QC to one injection of every 20 real samples. The suggested method makes a large amount of metabolomics analysis practicable