Pacific decadal oscillation causes fewer near-equatorial cyclones in the North Indian Ocean.

Tropical cyclones do not form easily near the equator but can intensify rapidly, leaving little time for preparation. We investigate the number of near-equatorial (originating between 5°N and 11°N) tropical cyclones over the north Indian Ocean during post-monsoon season (October to December) over the past 60 years. The study reveals a marked 43% decline in the number of such cyclones in recent decades (1981-2010) compared to earlier (1951-1980). Here, we show this decline in tropical cyclone frequency is primarily due to the weakened low-level vorticity modulated by the Pacific Decadal Oscillation (PDO) and increased vertical wind shear. In the presence of low-latitude basin-wide warming and a favorable phase of the PDO, both the intensity and frequency of such cyclones are expected to increase. Such dramatic and unique changes in tropical cyclonic activity due to the interplay between natural variability and climate change call for appropriate planning and mitigation strategies.

Performance of high resolution (400 m) PM2.5 forecast over Delhi.

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to - 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions.

Characterization of atmospheric trace gases and water soluble inorganic chemical ions of PM1 and PM2.5 at Indira Gandhi International Airport, New Delhi during 2017-18 winter.

Water soluble inorganic chemical ions of PM1 and PM2.5 and atmospheric trace gases were monitored simultaneously on hourly resolution at Indira Gandhi International Airport (IGIA), Delhi during 8 December 2017-10 February 2018. Monitoring was made by MARGA (Monitoring AeRosol and Gases in ambient Air) under winter fog experiment (WIFEX) program of the Ministry of Earth Sciences (MoES), Government of India. The result based on the analysis of the data so generated reveals that Cl-, NH4+, NO3- and SO42- were dominant ions in order which collectively constituted 96.8 and 97.3% of the of the total measured ionic mass in PM1 and PM2.5 respectively. Their overall average concentrations in PM1 were 19.5 ± 19.7, 18.4 ± 10.5, 16.6 ± 8.7 and 10.3 ± 5.7 µg/m3 and in PM2.5 were 36.0 ± 33.9, 32.7 ± 17.2, 28.5 ± 13.6 and 19.9 ± 13.9 µg/m3. Average concentrations of HCl, HNO3, HNO2, SO2 and NH3 trace gases were 0.7 ± 0.3, 2.7 ± 1.1, 6.6 ± 4.7, 22.0 ± 12.3 and 25.7 ± 9.1 µg/m3 respectively. Weather parameters along with low mixing height played significant role in the occurrence of high concentration of these chemical species. NH4+ was the prime neutralizer of the acidic components and mostly occurred in (NH4)2SO4/NH4HSO4, NH4NO3 and NH4Cl molecular forms. Major sources of these chemical species were fossil fuel combustion in aviation activity and transportation, coal burning in thermal power plants, industrial processes and emissions from biomass burning and agro-based activity. The quality of air with respect to PM2.5 always remained deteriorated. It became alarming during low visibility period mainly due to high concentration of Cl-, NO3-, SO42- and NH4+. Both meteorological and chemical processes interactively fed each other which occasionally resulted in fog development and visibility degradation. The knowledge gained by this study will help in simulation of atmospheric processes which lead to fog development and dispersal in the Delhi region.

Volatile organic compound measurements point to fog-induced biomass burning feedback to air quality in the megacity of Delhi.

We report the first ambient measurements of thirteen VOCs for investigations of emissions and air quality during fog and non-fog wintertime conditions at a tower site (28.57° N, 77.11° E, 220 m amsl) in the megacity of Delhi. Measurements of acetonitrile (biomass burning (BB) tracer), isoprene (biogenic emission tracer in daytime), toluene (a traffic exhaust tracer) and benzene (emitted from BB and traffic), together with soluble and reactive oxygenated VOCs such as methanol, acetone and acetaldehyde were performed during the winters of 2015-16 and 2016-17, using proton transfer reaction mass spectrometry. Remarkably, ambient VOC composition changes during fog were not governed by solubility. Acetaldehyde, toluene, sum of C8-aromatics (e.g. xylenes), sum of C9-aromatics (e.g. trimethyl benzenes) decreased by ≥30% (>95% confidence interval), whereas acetonitrile and benzene showed significant increases by 20% (>70% confidence interval), even after accounting for boundary layer dilution. During fog, the lower temperatures appeared to induce an emissions feedback from enhanced open BB within Delhi for warming, releasing both gaseous and aerosol pollutants with consequences for fog chemistry, sustenance and intensity. The potential feedback is important to consider for improving current emission parametrizations in models used for predicting air quality and fog in such atmospheric environments.

Characterization and source identification of PM2.5 and its chemical and carbonaceous constituents during Winter Fog Experiment 2015-16 at Indira Gandhi International Airport, Delhi.

Data on mass concentration of PM2.5 and its carbonaceous and water soluble inorganic chemical ions were compiled through sampling of PM2.5 at Indira Gandhi International Airport, Delhi during Dec. 16, 2015-Feb. 15, 2016 under Winter Fog Experiment (WIFEX) program of the Ministry of Earth Sciences (MoES) and analysing the samples. The data so generated were interpreted in terms of their variation on different time scales and apportioning their sources. It is found that mass concentration of PM2.5 averaged over the whole period of observation was 198.6±55.6. The concentration of organic carbon (OC) and elemental carbon (EC) was 24.7±9.4 and 11.7±4.7µg/m3 respectively with no any trend of increase or decrease over the observational period. SO42-, Cl- and NO3- dominated over other anions with their overall average concentration 34.0±23.1, 32.7±16.1 and 13.3±8.7µg/m3 respectively. Among cations, NH4+ showed highest concentration with an average value of 21.0±10.6µg/m3. Variation of daily average mass concentration of these parameters over the period of observation matched well with the variation of PM2.5 mass concentration indicating thereby to be the major contributors to the PM2.5 mass. NH4+ mostly occurred as NH4Cl and NH4NO3 and poorly as (NH4)2SO4 or NH4HSO4. H+ ion mostly occurred as H2SO4 and occasionally as HNO3. Carbonaceous aerosols and NO3- were mainly generated from fossil-fuel combustion. NH4+ and anthropogenic Cl- were mostly generated by biomass burning. The source of SO42- was found to be industries and thermal power plants. Continental Ca2+ and Mg2+ originated from thermal power plants and soil dust.

High-resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region.

High-resolution soil moisture/temperature (SM/ST) are critical components of the growing demand for fine-scale products over the Indian monsoon region (IMR) which has diverse land-surface characteristics. This demand is fueled by findings that improved representation of land-state help improve rainfall/flood prediction. Here we report on the development of a high-resolution (4 km and 3 hourly) SM/ST product for 2001-2014 during Indian monsoon seasons (June-September). First, the quality of atmospheric fields from five reanalysis sources was examined to identify realistic forcing to a land data assimilation system (LDAS). The evaluation of developed SM/ST against observations highlighted the importance of quality forcing fields. There is a significant relation between the forcing error and the errors in the SM/ST. A combination of forcing fields was used to develop 14-years of SM/ST data. This dataset captured inter-annual, intra-seasonal, and diurnal variations under different monsoon conditions. When the mesoscale model was initialized using the SM/ST data, improved simulations of heavy rain events was evident, demonstrating the value of the data over IMR.

Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall.

This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 °C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Niño Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.

A threefold rise in widespread extreme rain events over central India.

Socioeconomic challenges continue to mount for half a billion residents of central India because of a decline in the total rainfall and a concurrent rise in the magnitude and frequency of extreme rainfall events. Alongside a weakening monsoon circulation, the locally available moisture and the frequency of moisture-laden depressions from the Bay of Bengal have also declined. Here we show that despite these negative trends, there is a threefold increase in widespread extreme rain events over central India during 1950-2015. The rise in these events is due to an increasing variability of the low-level monsoon westerlies over the Arabian Sea, driving surges of moisture supply, leading to extreme rainfall episodes across the entire central subcontinent. The homogeneity of these severe weather events and their association with the ocean temperatures underscores the potential predictability of these events by two-to-three weeks, which offers hope in mitigating their catastrophic impact on life, agriculture and property.Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950-2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization.

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions.

ENSO's far reaching connection to Indian cold waves.

During boreal winters, cold waves over India are primarily due to transport of cold air from higher latitudes. However, the processes associated with these cold waves are not yet clearly understood. Here by diagnosing a suite of datasets, we explore the mechanisms leading to the development and maintenance of these cold waves. Two types of cold waves are identified based on observed minimum surface temperature and statistical analysis. The first type (TYPE1), also the dominant one, depicts colder than normal temperatures covering most parts of the country while the second type (TYPE2) is more regional, with significant cold temperatures only noticeable over northwest India. Quite interestingly the first (second) type is associated with La Niña (El Niño) like conditions, suggesting that both phases of ENSO provide a favorable background for the occurrence of cold waves over India. During TYPE1 cold wave events, a low-level cyclonic anomaly generated over the Indian region as an atmospheric response to the equatorial convective anomalies is seen advecting cold temperatures into India and maintaining the cold waves. In TYPE2 cold waves, a cyclonic anomaly generated over west India anomalously brings cold winds to northwest India causing cold waves only in those parts.

On the Variability and Increasing Trends of Heat Waves over India.

Over India, heat waves occur during the summer months of April to June. A gridded daily temperature data set for the period, 1961-2013 has been analyzed to examine the variability and trends in heat waves over India. For identifying heat waves, the Excess Heat Factor (EHF) and 90(th) percentile of maximum temperatures were used. Over central and northwestern parts of the country, frequency, total duration and maximum duration of heat waves are increasing. Anomalous persistent high with anti-cyclonic flow, supplemented with clear skies and depleted soil moisture are primarily responsible for the occurrence of heat waves over India. Variability of heat waves over India is influenced by both the tropical Indian Ocean and central Pacific SST anomalies. The warming of the tropical Indian Ocean and more frequent El Nino events in future may further lead to more frequent and longer lasting heat waves over India.

Anatomy of Indian heatwaves.

India suffers from major heatwaves during March-June. The rising trend of number of intense heatwaves in recent decades has been vaguely attributed to global warming. Since the heat waves have a serious effect on human mortality, root causes of these heatwaves need to be clarified. Based on the observed patterns and statistical analyses of the maximum temperature variability, we identified two types of heatwaves. The first-type of heatwave over the north-central India is found to be associated with blocking over the North Atlantic. The blocking over North Atlantic results in a cyclonic anomaly west of North Africa at upper levels. The stretching of vorticity generates a Rossby wave source of anomalous Rossby waves near the entrance of the African Jet. The resulting quasi-stationary Rossby wave-train along the Jet has a positive phase over Indian subcontinent causing anomalous sinking motion and thereby heatwave conditions over India. On the other hand, the second-type of heatwave over the coastal eastern India is found to be due to the anomalous Matsuno-Gill response to the anomalous cooling in the Pacific. The Matsuno-Gill response is such that it generates northwesterly anomalies over the landmass reducing the land-sea breeze, resulting in heatwaves.

Possible shift in the ENSO-Indian monsoon rainfall relationship under future global warming.

EI Nino-Southern Oscillation (ENSO) and Indian monsoon rainfall are known to have an inverse relationship, which we have observed in the rainfall spectrum exhibiting a spectral dip in 3-5 y period band. It is well documented that El Nino events are known to be associated with deficit rainfall. Our analysis reveals that this spectral dip (3-5 y) is likely to shift to shorter periods (2.5-3 y) in future, suggesting a possible shift in the relationship between ENSO and monsoon rainfall. Spectral analysis of future climate projections by 20 Coupled Model Intercomparison project 5 (CMIP5) models are employed in order to corroborate our findings. Change in spectral dip speculates early occurrence of drought events in future due to multiple factors of global warming.

Glucocorticoid receptor polymorphisms and haplotypes associated with chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is a significant public health problem of unknown etiology, the pathophysiology has not been elucidated, and there are no characteristic physical signs or laboratory abnormalities. Some studies have indicated an association of CFS with deregulation of immune functions and hypothalamic-pituitary-adrenal (HPA) axis activity. In this study, we examined the association of sequence variations in the glucocorticoid receptor gene (NR3C1) with CFS because NR3C1 is a major effector of the HPA axis. There were 137 study participants (40 with CFS, 55 with insufficient symptoms or fatigue, termed as ISF, and 42 non-fatigued controls) who were clinically evaluated and identified from the general population of Wichita, KS. Nine single nucleotide polymorphisms (SNPs) in NR3C1 were tested for association of polymorphisms and haplotypes with CFS. We observed an association of multiple SNPs with chronic fatigue compared to non-fatigued (NF) subjects (P < 0.05) and found similar associations with quantitative assessments of functional impairment (by the SF-36), with fatigue (by the Multidimensional Fatigue Inventory) and with symptoms (assessed by the Centers for Disease Control Symptom Inventory). Subjects homozygous for the major allele of all associated SNPs were at increased risk for CFS with odds ratios ranging from 2.61 (CI 1.05-6.45) to 3.00 (CI 1.12-8.05). Five SNPs, covering a region of approximately 80 kb, demonstrated high linkage disequilibrium (LD) in CFS, but LD gradually declined in ISF to NF subjects. Furthermore, haplotype analysis of the region in LD identified two associated haplotypes with opposite alleles: one protective and the other conferring risk of CFS. These results demonstrate NR3C1 as a potential mediator of chronic fatigue, and implicate variations in the 5' region of NR3C1 as a possible mechanism through which the alterations in HPA axis regulation and behavioural characteristics of CFS may manifest.

Improved detection of viral RNA isolated from liquid-based cytology samples.

BACKGROUND: Molecular diagnosis requires the ability to obtain high-quality nucleic acids that are representative of the disease state. We evaluated the recovery and detection of limiting amounts of viral oncogenic RNA from cells fixed in liquid-based cytology media. METHODS AND RESULTS: Serial dilutions of a human papillomavirus (HPV)-positive cell line fixed in a liquid media was used as a model system. Total nucleic acid (TNA) extraction produced RNA with clearly visible ribosomal bands even after one year of storage. These TNA extracts, treated with DNase-I, were used in an RT-PCR assay for HPV-16 E6-E7 oncogenic transcripts. With chemiluminscent Southern blot detection, samples with one HPV-positive cell in 30,000 were consistently detected. CONCLUSION: PreservCyt-fixed cells can yield RNA suitable for molecular assays even after one year of storage.

Validation of array-based gene expression profiles by real-time (kinetic) RT-PCR.

We evaluated real-time (kinetic) reverse transcription-polymerase chain reaction (RT-PCR) to validate differentially expressed genes identified by DNA arrays. Gene expression of two keratinocyte subclones differing in the physical state of human papillomavirus (episomal or integrated) was used as a model system. High-density filter arrays identified 444 of 588 genes as either negative or expressed with less than twofold difference, and the other 144 genes as expressed uniquely or with more than twofold difference between the two subclones. Real-time RT-PCR used LightCycler-based SYBR Green I dye detection and melting curve analysis to validate the relative change in gene expression. Real-time RT-PCR confirmed the change in expression of 17 of 24 (71%) genes identified by high-density filter arrays. Genes with strong hybridization signals and at least twofold difference were likely to be validated by real-time RT-PCR. This data suggests that (i) both hybridization intensity and the level of differential expression determine the likelihood of validating high-density filter array results and (ii) genes identified by DNA arrays with a two- to fourfold difference in expression cannot be eliminated as false nor be accepted as true without validation. Real-time RT-PCR based on LightCycler technology is well-suited to validate DNA array results because it is quantitative, rapid, and requires 1000-fold less RNA than conventional assays.

Use of real-time quantitative PCR to validate the results of cDNA array and differential display PCR technologies.

Real-time reverse transcription polymerase chain reaction (RT-PCR) methods that monitor product accumulation were adapted for the validation of differentially expressed genes. We describe a real-time quantitative PCR assay that uses SYBR Green I dye-based detection and product melting curve analysis to validate differentially expressed genes identified by gene expression profiling technologies. Since SYBR Green I dye is a nonspecific intercalating dye, the reaction is made specific by using "hot-start" PCR and empirically determined annealing and signal acquisition temperatures for each gene-specific primer. Relative expression levels were quantified by constructing a standard curve using cDNA dilutions of a highly expressed gene. Using this approach, real-time PCR validated 17 of 21 (71%) genes identified by DNA arrays, and all but 1 of 13 (91%) genes identified by differential display PCR (DD-PCR). Validation of differentially expressed genes detected by array analysis was related to hybridization intensity. Real-time RT-PCR results suggest that genes identified by DNA arrays with a two to fourfold difference in expression cannot be accepted as true or false without validation. Validation of differentially expressed genes detected by DD-PCR was not affected by band intensities. Regardless of the gene expression profiling technology (microarrays, DD-PCR, serial analysis of gene expression and subtraction hybridization), once the sequence of gene of interest is known, the real-time RT-PCR approach is well suited for validation of differential expression since it is quantitative and rapid and requires 1000-fold less RNA than conventional assays.

Alternative transcript initiation and novel post-transcriptional processing of a leucine-rich repeat receptor-like protein kinase gene that responds to short-day photoperiodic floral induction in morning glory (Ipomoea nil).

A gene (inrpk1) encoding a putative receptor-like protein kinase was isolated from the Japanese morning glory, Ipo-moea (Pharbitis) nil Roth. cv. Violet. The receptor-like portion of the largest derived polypeptide contains 26 direct leucine-rich repeats (LRRs) in a single block, and the catalytic portion has all the conserved amino acid residues characteristic of Ser/Thr protein kinases. RNA blot analysis detected multiple transcripts in cotyledons. The largest (4.4 kb) transcript encodes the predicted full length polypeptide (INRPK1), whereas a 1.6 kb transcript apparently originates from a secondary transcription initiation site within the gene and potentially encodes a protein kinase identical to INRPK1 but lacking most of the LRRs. Two transcripts (ca. 2.7 and 2.6 kb) are created by alternative 3'-splicing of a large (ca. 1.4-1.5 kb) cryptic intron in the LRR region, creating one transcript (2.6 kb) potentially encoding a small, secretable polypeptide. The larger transcript encoding a polypeptide identical to INRPK1, but lacking 21 LRRs, predominates in vegetative roots. Competitive PCR indicates that inrpk1 mRNA increases 20-fold in cotyledons in response to a previously given single floral-inducing short-day (SD). No differences of this magnitude were detected in any other organs examined from plants similarly treated. This pattern of expression and differential processing suggests a role for inrpk1 in some aspect of SD photoperiodic-induced flowering in morning glory.

Reproducibility of alternative probe synthesis approaches for gene expression profiling with arrays.

Before gene expression profiling with microarray technology can be transferred to the diagnostic setting, we must have alternative approaches for synthesizing probe from limited RNA samples, and we must understand the limits of reproducibility in interpreting gene expression results. The current gold standard of probes for use with both microarrays and high-density filter arrays are synthesized from 1 microg of purified poly(A)+ RNA. We evaluated two approaches for synthesizing cDNA probes from total RNA with subsequent hybridization to high-density filter arrays: 1) reverse transcription (RT) of 5 microg total RNA and 2) RT-polymerase chain reaction (RT-PCR) of 1 microg total RNA, using the SMART system. The reproducibility of these two approaches was compared to the current gold standard. All three methods were highly reproducible. Triplicate experiments resulted in the following concordance correlation coefficients to evaluate reproducibility: 0.88 for the gold standard, 0.86 for cDNA probe synthesized by RT from total RNA, and 0.96 for the SMART cDNA probe synthesized from total RNA. We also compared the expression profile of 588 genes for the total RNA methods to that obtained with the gold standard. Of 150 positive genes detected by the gold standard, 97 (65%) were detected by cDNA probe synthesized by RT of total RNA, and 122 (81%) were detected by the SMART cDNA probe. We conclude that SMART cDNA probe produces highly reproducible results and yields gene expression profiles that represent the majority of transcripts detected with the gold standard.