Southwest Monsoon Season Research Articles

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

AbstractThe accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.

Rainfall Trend Investigation of Hemavati Catchment, Karnataka, India

The rainfall trend investigation study supports to judge the periodic pattern of rainfall. The quantifiable evaluation of the overall impacts of different elements, for example, changes in precipitation, temperature, evapotranspiration, environmental changes encouraged by a hazardous atmospheric deviation and human activities on the streamflow is critical for territorial appraisal of water assets and its feasible management. This study focusses on to analyze the seasonal rainfall trend in Hemavati Catchment, Karnataka, India. For the present study, thirty-two years (1990-2021) of seasonal rainfall trend has been examined using the Mann-Kendall assessment and Sen’s gradient estimation methods. The investigation has been carried out for the cold weather season (December-February), hot weather season (March-May), southwest monsoon season (June-September), and post–monsoon season (October–November) by the use of XLSTAT software and the study demonstrations that here is a varied trend in the catchment.

Seasonal benthic species composition linked to coastal defense structures (CDS) in Kuala Nerus, Terengganu, Malaysia.

The natural hydrodynamic process of Kuala Nerus, Terengganu, has changed since the extension of Sultan Mahmud Airport runway in 2008. Consequently, severe coastal erosion has occurred in the area, particularly during the northeast monsoon season (NEM). Numerous types of coastal defense structures (CDS) have been constructed to protect the coastline. Despite the loss of esthetic values, the effect of CDS construction on marine organisms in the area remains unknown. Hence, this study aims to assess the ecological aspects of macrobenthic compositions at the CDS area of Kuala Nerus, Terengganu, based on the differences between the southwest (SWM) and northeast (NEM) monsoon seasons. Macrobenthos were collected from the sediment in July (SWM) and December 2021 (NEM) using the Ponar grab at 12 substations from five sampling stations. The density of macrobenthos was higher in SWM (48,190.82 ind./m2) than in NEM (24,504.83 ind./m2), with phylum Mollusca recording the highest species composition (60-99.3%). The macrobenthos species had a low to moderate level of diversity (H' = 1.4-3.1) with the species were almost evenly distributed (J' = 0.2-0.8). Windward substations exhibited coarser grain sizes (38.56%-86.84%), whereas landward substations exhibited very fine grain sizes (44.26%-86.70%). The SWM season recorded a higher organic matter content (1.6%-6.33%) than the NEM season (0.4%-3.1%). However, metal concentrations in the surface sediment were within the safe range and permissible limits for both seasons, inferring that the macrobenthos composition was unaffected. This study demonstrated that the CDS associated with the monsoon system has controlled the hydrodynamics and nearshore sedimentary processes in the Kuala Nerus coastal zone, thereby affecting the macrobenthos population, in terms of richness and density. The ecological and energetic effects of the coastal structures in different seasons have resulted in a more significant result, with the SWM exhibiting a higher macrobenthos composition than the NEM.

Variation of amino acid in the sinking particulates in the northeastern Arabian Sea and the northern Bay of Bengal

Sediment traps are widely used to get insight into how particulate matter forms and settles in the water column. Earlier studies in the Northern Indian Ocean have shown that the sinking flux depends mainly on surface primary production and varies seasonally and inter-annually. In the case of the Bay of Bengal, river runoff plays a significant role in the sinking flux. As a part of the SIBER-INDIA program, a mooring consisting of sediment traps was deployed at three depths (495 m, 1205 m, and 2915 m) in the Arabian Sea (Northeastern Arabian Sea sediment trap, NEAST;16.87°N, 67.77°E) and at 580 m, 1110 m, and 1635 m in the Bay of Bengal (Bay of Bengal sediment trap, BoBST; 18.21°N, 89.63°E). We present here data on total fluxes, concentrations of particulate carbon and nitrogen, biogenic opal, and amino acid composition for the period 2017–2018 in both basins. Particle flux in the Arabian Sea was influenced by monsoon-related processes with higher flux during the northeast monsoon (NEM) and southwest monsoon (SWM) seasons, whereas the flux was higher in BoB during SWM. Particulate nitrogen (PN), particulate organic carbon (POC), and total hydrolysable amino acid (THAA) mostly decreased with depth in all traps due to the decomposition of labile organic constituents in the sinking flux. Changes in the decomposition of organic matter with depth were further reflected in the POC/PN molar ratios, which increased with depth in the Arabian Sea. A low POC/PN molar ratio associated with the higher THAA concentration in most of the shallow traps indicates the presence of fresher organic matter. Among the THAA, Glycine (Gly) was the dominant amino acid in all the samples followed by Aspartic acid (Asp), Glutamic acid (Glu), Serine (Ser), and Alanine (Ala). The amino acid-based degradation index (DI) reveals the degradation of labile organic matter with depth in the NEAST. However, the middle trap in the BoB showed a different trend with higher total and lithogenic fluxes, POC, PN, and DI values compared to the shallow trap indicating lateral advection in the bay. Monsoonal changes and mesoscale eddies make these basins distinct from one another and influence their sinking fluxes and biogeochemical settings.

Spatio-temporal variability of surface chlorophyll and pCO2 over the tropical Indian Ocean and its long-term trend using CMIP6 models

Under the global climate change scenario, ever-increasing ocean temperatures and elevated carbon dioxide (CO2) concentration levels in the ocean and atmosphere significantly affect ocean biogeochemistry. The spatiotemporal distribution of chlorophyll and partial pressure of CO2 (pCO2) over the Indian Ocean (IO) are investigated using satellite, in-situ, and simulations from eleven Coupled Model Inter-comparison Project phase 6 models. All the models show negative bias near the Somali region over the Arabian Sea (AS) during the southwest monsoon season. The CESMs and CanESM5 models show relatively less bias over the tropical IO except for the AS in all seasons compared to other models. The annual cycle of pCO2 shows a bimodal characteristic, with the first peak in May and the other in October over the northern IO, which the CanESM5, IPSL, and MPIs models reasonably well capture. All the models produce the phase of the annual cycle of pCO2 reasonably well for the southern IO. The pCO2 distribution and its trend decomposition are estimated using the multimodel mean from the CanESM5, IPSL-CM6A-LR, and MPIs models, suggesting that the increase in dissolved inorganic carbon is the dominant factor that contributes to about 70 % of the rise in the total pCO2 trend, and the total alkalinity and sea surface temperature have a secondary role. Region-wise analysis manifests that the southern IO and AS exhibit maximum susceptibility to the long-term variations in the pCO2 trend caused by the changes in dissolved inorganic carbon levels in the ocean. The study discusses substantive factors leading to the variability in the biogeochemical properties of the IO as simulated by these models and, thus, the possibilities for future improvements.

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter (PM10 and PM2.5) in urban and industrial areas in Malaysia during haze episodes, which typically occur in the south west monsoon season. The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries. Daily PM concentrations were analysed for urban and industrial areas including Alor Setar, Tasek, Shah Alam, Klang, Bandaraya Melaka, Larkin, Balok Baru, and Kuala Terengganu in 2018 and 2019. The analysis employed spatiotemporal to examine how PM levels were distributed. The data summary revealed that PM levels in all study areas were right-skewed, indicating the occurrence of high particulate events. Significant peaks in PM concentrations during haze events were consistently observed between June and October, encompassing the south west monsoon and inter-monsoon periods. The study on acute respiratory illnesses primarily focused on Selangor. Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma (AEBA) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) with values of 260.500 and 185.170, respectively. Similarly, for outpatient cases of AEBA and AECOPD, Klang had the highest average values of 41.67 and 14.00, respectively. Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning. The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions, particularly from June to September, as shown in the bar diagram. Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses, seen in higher inpatient and outpatient visits (p < 0.05). However, seasonal variability had minimal impact on healthcare utilization. These findings offer a comprehensive assessment of PM levels during historic haze episodes, providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

Seasonal Modulation of the Madden–Julian Oscillation’s Impact on Rainfall in Sri Lanka

Abstract For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method. Significance Statement Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.

Using of Eggenberger–Polya Distribution as an Alternative Model for Dry Spell Analysis in Northern Odisha, India

In this paper, Eggenberger–Polya distribution has been preferred as an alternative to a 2-state Markov Chain model for characterizing persistence behaviour of dry spell for Northern Odisha, India during south-west monsoon season. An attempt has been made to compare performance of the two competing models with reference to their strength to fit the empirical distribution of dry spell length under two goodness-of-fit criteria.

Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

AbstractGlobal climate model (GCM) projections are subject to significant uncertainties. Quantifying uncertainties in climate change projections improves credibility and makes climate data more reliable. This study aims to quantify the uncertainties in projected extreme precipitation during the 21st century over the homogeneous rainfall regions of India simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs. The percentile‐based square root error variance (SREV) method estimates model, scenario and ensemble uncertainties in projections of extreme precipitation. The uncertainty is investigated at four thresholds: 95th, 99th, 99.9th and 100th percentiles. The results show that the wet northeast region has a greater SREV, which is consistent with previous studies. At 99th and 99.9th percentiles, relative model SREV is dominant over the northeast (NE) region. However, at the 95th percentile high relative model SREV is found over the northwest (NW) region during southwest (June, July, August and September) and NE (October, November and December) monsoon seasons. Model uncertainty is the main source of uncertainty, followed by scenario and ensemble uncertainties. The study indicates that the arid NW region in India has a higher level of uncertainty than other regions with homogeneous rainfall. These findings will assist policymakers in planning infrastructure development in arid regions of India.

A CLIMATIC PREDICTABILITY INDEX FOR SOUTH WEST MONSOON SEASON IN DIFFERENT DISTRICTS OF WEST BENGAL WITH APPLICATION OF FRACTAL DIMENSION ANALYSIS

A CLIMATIC PREDICTABILITY INDEX FOR SOUTH WEST MONSOON SEASON IN DIFFERENT DISTRICTS OF WEST BENGAL WITH APPLICATION OF FRACTAL DIMENSION ANALYSIS

Monsoon-driven seasonal hypoxia along the northern coast of Oman

Dissolved oxygen and current observations from a cabled ocean observatory in the Sea of Oman show that the annual recurrence of coastal hypoxia, defined as dissolved oxygen concentrations ≤63 μM, is associated with the seasonal cycle of local monsoon winds. The observations represent the first long-term (5+ years) continuous moored observations off the northern Omani coast. During the summer/fall southwest (SW) monsoon season (Jun-Nov), winds in the Sea of Oman generate ocean currents that result in coastal upwelling of subsurface waters with low dissolved oxygen concentrations. The source of the poorly oxygenated water is the oxygen minimum zone (OMZ) in the Arabian Sea, a layer approximately 1000-m thick within the 100 to 1200 m depth range, where dissolved oxygen values approach anoxia. During the winter monsoon season (Dec-Feb), the Sea of Oman winds are from the northwest, forcing strong and persistent southeast currents. These winds generate oceanic downwelling conditions along the coastal ocean that ventilate waters at depth. Possible impacts of the monsoon-driven seasonal hypoxia on local fisheries and implications due to climate change are also discussed in this study.

Hydrogeochemical and multivariate statistical approaches to investigate the characteristics of groundwater quality in fluoride-enriched hard rock region in Tirupathur district of Tamil Nadu, India.

Fluoride pollution is a major issue worldwide, posing health risks such as dental and skeletal fluorosis. This study was conducted in fluoride enrichment hard rock regions of Vaniyambadi and Ambur talks in Tirupathur district, Tamil Nadu. Four hundred eighty groundwater samples were collected from May 2021 to April 2022 and grouped as summer, southwest monsoon (SWM), northeast monsoon (NEM), and winter. Maximum concentration of fluoride was found to be 4mg/L in the summer season in Vellakuttai region of Vaniyambadi taluk. The study aims to investigate the hydrogeochemical process and mechanism influencing groundwater chemistry and it also provides the confirmation of exploratory data analysis in groundwater quality using structural equation modeling. The Piper and Gibbs diagrams illustrate the rock-water interaction and anthropogenic sources that contribute to the NaHCO3 and NaCl-type waters, respectively. Multivariate statistical analysis such as hierarchical cluster analysis (HCA), principal component analysis (PCA), multiple linear regression (MLR), and structural equation modeling (SEM) has been carried out to determine the groundwater quality. HCA manifests the nature and sources of groundwater, whereas PCA divides all the physicochemical parameters into two PC loadings, accounting for 97.46%, 99.46%, 99.18%, and 98.93% of cumulative % of variance during the summer, SWM, NEM, and winter seasons, respectively. PC1 has a higher loading factor to Cl, Ca, and Mg, whereas PC2 has a higher loading factor to Na, HCO3, SO4, and NO3. The results of the MLR model provide higher accuracy in detecting the contamination factors associated with the environment and natural rocks. SEM revealed the goodness-of-fit indices 0.993, 0.999, 1.000, and 0.999 in summer, SWM, NEM, and winter, respectively. Hence, this study provides insight view of variation of fluoride concentration in groundwater in different seasons and also mentions the factors that influence fluoride concentration in Vaniyambadi and Ambur taluk.

Genetic elucidations of grain iron, zinc and agronomic traits by generation mean analysis in pearl millet [Pennisetum glaucum (L.) R. Br.

Pearl millet is endowed with important nutritional attributes and climate resilience and has been identified as potential staple food crop to address nutritional and food security. Gene action studies for different traits will assist in devising suitable breeding strategies. The present study was conducted to determine gene actions for grain iron and zinc concentrations and agro-morphological traits using generation mean analysis for biofortified pearl millet breeding. Six generations P1, P2, F1, F2, BC1 and BC2 were evaluated during South-west monsoon season 2021 and 2022. Analysis of variance showed significant variability for all the traits in both seasons. Six parameter models revealed the predominance of additive and dominant gene effects and non-allelic interactions for plant height, spike length, spike girth 1000- seed weight, iron and zinc content. Seed yield per spike was predominantly under non-additive gene control (maximum in cross I where d was 29.53 and l was −49.53); however, additive genetic control also contributed significantly. Likewise, for day to 50% flowering, days to maturity and number of productive tillers, dominant and additive × dominance type of gene effects were substantial. These findings provide deeper insights for strategizing breeding methods to increase iron and zinc content as well as yield potentially.

Distribution of artisanal fishing gears with impacts on sedimentation and benthic macroinvertebrate assemblages in Songkhla Lake, the largest lagoon in Thailand

The present study investigated the distribution and diversity of stationary fishing gears in Songkhla Lake and assessed the effects of the most abundant gear in the lake, the standing trap, on sedimentation and the abundance of benthic macroinvertebrates. Our survey revealed that six types of fishing gear were used: standing traps, fyke nets, fish aggregating devices, seabass net cages, small traps and stationary lift nets. The type of fishing gear used depended on the depth of water. The density of installed standing traps reached a maximum of 357 traps/km2, which was the highest density of any fishing gear installed on the lake. They were mainly set in the middle part of the lake, where the water was less than 2 m deep. Sedimentation was examined inside and outside the areas influenced by standing traps. Sediment samples were collected with sediment traps in the northeast and southwest monsoon seasons. Sedimentation rates inside and outside the areas influenced by standing traps differed at some sites but the pattern was not consistent across seasons. Sedimentation rates were higher in the northeast monsoon season with the average rate 1132.32 ± 43.73 (mean ± SE) g m−2day−1 when precipitation and run-off were higher and 323.98 ± 10.68 g m−2day−1 in the southwest monsoon or dry season. Moreover, the proportion of finer clay particles was greater in the sediment from the areas influenced by traps. Benthic macroinvertebrate communities between the two areas were different. A bivalve, Potamocorbula sp., which dominated benthic macroinvertebrate assemblages, had a higher abundance in areas influenced by traps. The effects of stationary fishing gears on sedimentation and benthic macroinvertebrates highlighted in this study suggest that the long-term deployment of these gears may affect food chain dynamics in this lagoon system. Ultimately, a proper regulation of the use of these gears may be needed.

The Influence of Weather Patterns and the Madden‐Julian Oscillation on Extreme Precipitation Over Sri Lanka

AbstractSri Lanka is affected by extreme precipitation events every year, which cause floods, landslides and tremendous economic losses. We use the ERA5 reanalysis data set to understand the association of extreme precipitation events with 30 weather patterns, which were originally derived to represent the variability of the Indian climate during January–December 1979–2016. We find that weather patterns that are most common during the northeast monsoon (December–February) and second intermonsoon (October–November) seasons produce the highest number of extreme precipitation events. Furthermore, extreme precipitation events occurring during these two seasons are more persistent than those during the southwest monsoon (May–September) and first intermonsoon (March–April) seasons. We analyze the modulation of extreme precipitation events by the Madden‐Julian Oscillation, and find that their frequency is enhanced (suppressed) in phases 1–4 (5–8) for most weather patterns.

A satellite-observation based study on responses of clouds to aerosols over South Asia during IOD events of south-west monsoon season

The present study investigated the possible effects of aerosols on cloud properties during positive and negative Indian Ocean Dipole (IOD) months over South Asia using 20 years (2002–2021) of long-term satellite-based observations and reanalyses datasets. Positive aerosol optical depth (AOD) differences between the two IOD phases are found mainly over the land areas and some parts of the Indian Ocean, with the maximum value observed over Indonesia, Malaysia, Northern China, Western India, and the Northern Arabian Sea. The negative AOD differences are seen over the equatorial Indian Ocean, some regions of Indian land mass, the North Indian Ocean, and some parts of the South China Sea, with the highest dip over the central Indo-Gangetic Plain region. The highest increase (decrease) in rainfall is observed over the western (eastern) pole. The reduced rainfall is responsible for the increased AOD over Indonesia, Malaysia, and neighboring regions. The strong negative relationships of cloud fraction (CF) with cloud top pressure and cloud optical depth (COD) suggest persisting optically thick middle and high-level clouds during the IOD months. Twomey effect is noticed over the study region during both the IOD phases irrespective of the meteorological conditions. CF and COD both showed an upsurge with increased AOD in the presence of high relative humidity and stronger updrafts, suggesting the role of prevailing meteorology in forming thicker clouds. However, the height of the cloud tops is getting lowered in polluted conditions. The aerosol-cloud relationships observed during IOD scenarios could be attributed to the prevailing meteorology.

Source apportionment of PM2.5 in Thailand's deep south by principal component analysis and impact of transboundary haze.

Atmospheric particulate matter smaller than 2.5 micron (PM2.5) was evaluated at four sites in the lower southern part of Thailand during 2019-2020 to understand the impact of PM2.5 transport from peatland fires in Indonesia on air quality during the southwest monsoon season. Mass concentration and chemical bound-PM, including carbon composition, e.g., organic carbon (OC) and elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and inorganic elements, were analyzed. The PM2.5 emission sources were identified by principal components analysis (PCA). The average mass concentrations of PM2.5 in the normal period, which represents clean background air, from four sites was 3.5-5.1µg/m3, whereas during the haze period, it rose to 5.4-13.5µg/m3. During the haze period, both OC and EC were 3.5 times as high as in the normal period. The average total PAHs and BaP-TEQ of PM2.5 during the haze period were ~ 1.3-1.7 and ~ 1.2-1.9 times higher than those in the normal period. The K concentrations significantly increased during haze periods. SO42- dominated throughout the year. The effects of external sources, especially the transboundary haze from peatland fires, were significantly enhanced, because the background air in the study locations was generally clean. PCA indicated that vehicle emission, local biomass burning, and secondary particles played a key role during normal period, whereas open biomass burning dominated during the haze phenomena. This was consistent with the OC/EC and PAH diagnostic ratios. Backward trajectories confirmed that the sources of PM during the haze period were predominantly peatland fires in Sumatra, Indonesia, due to southwest wind.

Thermodynamic structure of the atmospheric boundary layer over a coastal station in India for contrasting sky conditions during different seasons

The thermodynamic structure of the atmospheric boundary layer for contrasting sky conditions over Chennai, a coastal station in the Indian subcontinent, is investigated through conserved variable analysis of equivalent potential temperature and specific humidity. Simultaneous radiosonde and micropulse lidar measurements undertaken in 2018 constitute the primary database in this investigation. One of the most prominent features of this analysis is a consistent occurrence of a double mixed layer structure during the clear-sky and cloudy conditions throughout the year. The occurrence frequency of the double mixing lines is higher during the pre-monsoon season compared with the winter and northeast (NE) monsoon seasons. The advection mainly dominates the formation of double mixing lines during the winter and pre-monsoon seasons. In contrast, convection and advection dominate during the southwest (SW) and NE monsoon seasons. The frequent double mixing lines over Chennai occur mainly from the restratification of the convective boundary layer (CBL) due to the sea-breeze onset and the cloud layer. Occasionally, a triple mixing line structure is also observed during the fair-weather boundary layer (FWBL) of the pre-monsoon and SW monsoon seasons. Among the 355 total observations collected during 2018, the first, second, and third mixing lines occurred 100%, 70%, and 14%, respectively. The thermal internal boundary layer (TIBL), CBL, and FWBL occur ∼50%, ∼97%, and ∼ 30%, respectively. The first mixing line is represented by both the TIBL and CBL, and CBL and FWBL represent the second mixing line, whereas the third mixing line is represented solely by the FWBL. The first and second mixing line shows strong seasonal variations with lower altitudes during the pre-monsoon season and higher altitudes in the SW monsoon season, almost in the same phase as the CBL variation but in the opposite phase of the TIBL variations. The CBL height attains a minimum during the winter season and maximum during the SW monsoon season, while TIBL becomes minimum during the pre-monsoon and SW monsoon seasons and maximum during winter and NE monsoon seasons.

Insights from the bias-corrected simulations of CMIP6 in India's future climate

In this study, we have used thirteen statistically downscaled bias-corrected General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathways (SSP) scenarios. Based on these models, the projected changes in mean rainfall and daily extremes (number of rainy days and simple daily intensity) during the southwest (SW) and northeast (NE) monsoon seasons in the near (2021–2050) and far future (2071–2100) under the SSP2-4.5 & SSP5-8.5 scenarios relative to the baseline period (1985–2014) are studied. In addition to the rainfall, the maximum, minimum, and mean temperature changes and their daily extremes are also examined. Our results show that the Multi-Model Mean (MMM) slightly underestimated (overestimated) the SW (NE) monsoon rainfall compared to the observed rainfall in the baseline period. A considerable increase in future rainfall during the SW monsoon season is noticed in central India, the Himalayan region, and over the northwestern parts of India. In the NE monsoon season, south peninsular India experiences more rainfall under SSP5-8.5 than SSP2-4.5. In general, both SSPs scenarios shows increase in monsoon rainfall from the mid-century onwards. Interestingly, by the end of the 21st century the number of rainy days are projected to reduce, whereas the intensity of rainfall projected to be drastically increase over many areas of India. Analysis of temperature revealed that there is increase in the projected warming with maximum temperature is around 4.5°C during summertime, with minimum temperatures of about 5°C in the wintertime in northern parts of India by the end of the 21st century. Under the SSP5-8.5 scenario, increase in the highest maximum temperatures is seen in the Himalayan region and elevated minimum temperature pattern over the Indo-Gangetic Basin (IGB) is noted. This increased warming may affect the agriculture, water, health, and power sectors severely.

Chayote leaves display seasonal variation in phytoestrogenic activities and phenolic content

Chayote (Sicyos edulis Jacq.) is a popular vegetable in the Philippines and has been recognized for its nutritional value and bioactive compounds, including phenolic acids found in its edible parts. Hence, this study sought to determine whether extracts from S. edulis shoots can elicit a bioactive estrogenic response. Specifically, the study determined whether the location of cultivation or monsoon season of collection would influence the estrogenic activities of S. edulis extracts. Here, we found that a specific fraction (F5.6) of S. edulis shoots isolated through solid-phase extraction (C-18 and HLB) inhibited the transactivation of an estrogen-induced luciferase reporter gene in an estrogen receptor-positive cell line T47D without affecting cell viability, independently of the farm source and the season of sample collection. This inhibition was also seen in the E2- induced expression of superoxide dismutase 2 (SOD2), an antioxidant enzyme upregulated in advanced breast cancer cells. In contrast, the phenolic content in the F5.6 fraction varied across seasons, with a higher content found during the northeast monsoon season (0.175 ± 0.027 mg GAE/mg sample) than the southwest monsoon seasons (0.043 ± 0.015 and 0.051 ± 0.012 mg GAE/mg sample). The high phenolic content increased the ratio of estrogen receptor alpha to beta transcripts and the mRNA expression level of the estrogen-responsive gene, growth regulation by estrogen in breast cancer 1 (GREB1A), in T47D cells. Overall, this study showed how seasonal variation may affect the phenolic content of S. edulis and influence the plant’s bioactivity, providing a potential cultivation strategy in obtaining extracts with the intended bioactivity.