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The ionospheric anomalies are often responsible for compromising the accuracy and performance of Global Navigation Satellite System (GNSS). GPS Aided Geo Augmented Navigation (GAGAN) is the autonomous Space Based Augmentation System of India which serves to provide timely error detection and correction for the corrupted GNSS signals in aircrafts. This paper discusses a Maximum Minimum Eigen (MME) detector to identify the ionosphere induced errors in the GNSS signals. Data has been collected from 24 different GAGAN receivers spread over the length and breadth of India from January to December, 2015. The climatology of ionospheric anomaly over India, has been established using the MME detector. This paper discusses the spatio-temporal dependence of ionospheric anomalies over India. The observations indicate an increased ionospheric activity in the course of equinoxes and a comparitively quiet ionosphere in the course of solstices. A higher number of ionospheric disturbance incidents have been noticed closer to the anomaly crest. The results from the MME detector have been verified against the Rate of Total Electron Content (TEC) Index (ROTI) values and S4 observations for the year 2015 at the 24 GAGAN TEC stations. The disturbance climatology obtained from MME detector, ROTI and S4 exhibit a high correlation. However, the outcome of the MME detector clearly shows that it is capable of picking up even small irregularities in the ionospheric TEC. [ABSTRACT FROM AUTHOR]

Religious spaces are an integral part of Indian cities. Unique in their spatiality, they function as socio-cultural hubs drawing users from varied economic and social hierarchies. This study deals with physical and perceptional assessments of micrometeorological conditions in two religious squares namely Hanuman Mandir Square (HMS) and Gurudwara Bangla Sahib Square (GBS) located in New Delhi (28.6° N; 77.2° E), India. The study involved real-time physical measurement of environmental variables such as dry-bulb temperature (Ta, °C), globe temperature (Tg, °C), relative humidity (RH), and air velocity (Va). Variables such as physiological effective temperature (PET), universal thermal comfort index (UTCI), and mean radiant temperature (Tmrt, °C) were computed from measured variables. Concurrent thermal comfort surveys were carried out with 353 respondents in both the squares. The paper describes the thermal characteristics of the studied squares and presents the associated subjective thermal response and preferences of the users. PET was found to correlate well with the subjective responses. The neutral value of PET is found to be 24.7 °C. The neutral PET value of respondents visiting for non-worship purposes was found to be 2.7 °C lesser than those visiting for worship purposes. People visiting the squares for non-worship purpose however were found to be more tolerant of higher PET conditions as compared to others. Factors such as intent of visit, solar exposure, thermal history, and landscape elements were found to have a statistically significant influence on the thermal perception. The paper further summarizes the adaptive opportunities preferred by the users in order to improve thermal comfort in the studied squares. A weighted ranking of adaptive preferences reported by the respondents has also been presented. [ABSTRACT FROM AUTHOR]

India's position on climate change negotiations is likely to have far reaching implications for the success of global climate cooperation. Since the beginning of negotiations, the principle of equity and common but differentiated responsibilities (CBDR) remained the centerpiece of India's stand. The stand started to evolve at the 15th Conference of Parties to the United Nations Framework Convention on Climate Change at Copenhagen in 2009, when India accepted voluntary commitments to reduce emission intensity. Though India still swears by CBDR, status of the principle in the negotiations has become doubtful after the Durban Climate Conference in 2011 committed all parties to take emission targets. This paper traces major transition points in India's negotiating position over the years and provides a descriptive context of its climate-related concerns. It analyzes the interview responses of 15 top scientists, experts, and negotiators to build upon core areas of climate change issues in India, its future role, and position in negotiations. Interviewees, in general, were in favor of protecting the carbon space for the poor who had very low emissions. [ABSTRACT FROM AUTHOR]

This paper presents results of a study of long term trends in the characteristics of the within-season temporal profile of southwest monsoon rainfall over western India during the last five decades in relation to global warming induced regional climate change. In contrast to recent climate change analyses and projections, no significant long-term trends have been observed in this study. Slow decadal scale variations observed are analysed in relation to Pacific Decadal Oscillations (PDO). Daily variations in rainfall anomaly show opposite characteristics during negative and positive phases of PDO. The above-normal rainfall (>25%) is found during the starting phase of monsoon in negative PDO. Over the last decade, i.e., during 2000-2007, the seasonal rainfall amount, as well as seasonal span of southwest monsoon over western India is indicative of a gradual increase. [ABSTRACT FROM AUTHOR]

This paper analyzes the role of the Indian Ocean (IO) and the atmosphere biases in generating and sustaining large-scale precipitation biases over Central India (CI) during the Indian summer monsoon (ISM) in the climate forecast system version 2 (CFSv2) hindcasts that are produced by initializing the system each month from January 1982 to March 2011. The CFSv2 hindcasts are characterized by a systematic dry monsoon bias over CI that deteriorate with forecast lead-times and coexist with a wet bias in the tropical IO suggesting a large-scale interplay between coupled ocean-atmosphere and land biases. The biases evolving from spring-initialized forecasts are analyzed in detail to understand the evolution of summer biases. The northward migration of the Inter Tropical Convergence Zone (ITCZ) that typically crosses the equator in the IO sector during April in nature is delayed in the hindcasts when the forecast system is initialized in early spring. Our analyses show that the delay in the ITCZ coexists with wind and SST biases and the associated processes project onto the seasonal evolution of the coupled ocean-atmosphere features. This delay in conjunction with the SST and the wind biases during late spring and early summer contributes to excessive precipitation over the ocean and leading to a deficit in rainfall over CI throughout the summer. Attribution of bias to a specific component in a coupled forecast system is particularly challenging as seemingly independent biases from one component affect the other components or are affected by their feedbacks. In the spring-initialized forecasts, the buildup of deeper thermocline in association with warmer SSTs due to the enhanced Ekman pumping in the southwest IO inhibits the otherwise typical northward propagation of ITCZ in the month of April. Beyond this deficiency in the forecasts, two key ocean-atmosphere coupled mechanisms are identified; one in the Arabian Sea, where a positive windstress curl bias in conjunction with warmer SSTs lead to a weakening of Findlater jet and the other in the east equatorial IO where a remote forcing by the predominantly westerly bias in the western-central equatorial IO in the summer strengthen the seasonal downwelling Kelvin wave that in turn deepens the thermocline in the eastern IO. The equatorial Kelvin wave continues as a coastal Kelvin wave and disperses as Rossby waves off Sumatra and induces positive SST and precipitation biases in the eastern and southern Bay of Bengal. This study shows that the biases that first appear in winds lead to a cascade of coupled processes that exacerbate the subsequent biases by modulating the evolution of seasonal processes such as the annual Kelvin and Rossby waves and the cross-equatorial vertically integrated moisture transport. While this analysis does not offer any particular insights into improving the ISM forecasts, it is a foundational first step towards this goal. [ABSTRACT FROM AUTHOR]

This paper looks at the crucial issue of dry-season groundwater-availability in the state of Maharashtra, India. We look at the two key hydro-climatological measurements which are used to implement groundwater policy in the state, viz., water levels in 5000+ observation wells across the state and aggregate rainfall data. We see that there is substantial variation in groundwater levels within and across the years in most wells. We argue that for a large number of these observation well locations, aggregate rainfall data is inadequate to model or to predict groundwater levels. For this, we use a novel random rainfall coefficient model for the purpose of modelling the effect of rainfall in a composite setting where extraction and changing land-use data is unknown. The observed high variance of this coefficient points to significant variations in groundwater levels, which may only be explained by unmeasured anthropogenic factors. Next, we see that the uncertainty in actual groundwater levels along with scarcity are two distinct features of groundwater availability and will elicit different behaviours from the typical user. Finally, we recommend that quantitative groundwater assessment protocols of the state should move to incorporating data from which extraction and land-use may be modelled. We believe this is one of the first studies where large spatio-temporal scale data gathered by state agencies have been analysed for scientific adequacy. [ABSTRACT FROM AUTHOR]

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022–2059) and far (2060–2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors. [ABSTRACT FROM AUTHOR]

This study demonstrated the influence of downscaling using the regional climate model (RCM) driven by Era-Interim reanalysis (EIN) in simulating different aspects of the Indian summer monsoon (ISM). It is also examined, whether increasing the horizontal resolution of RCM will inevitably be capable of adding more information to ISM characteristics and its spatio-temporal variability. In this regard, two RCM (at 50 km: Reg50 and 25 km: Reg25) simulations were conducted for six years from 2000 to 2005 for the South Asia Coordinated Regional Downscaling Experiment (CORDEX) domain. The added value (AV) is found to be strongly dependent on region and considered metrics. A slight improvement towards increasing spatial resolution is observed in the simulation of the mean ISM characteristics, while considerable improvements are noticed for the frequency distribution of extremes. The notable improvement in the daily climatology of precipitation is observed over the region of northeast India (~ 35%) and the Hilly region (~ 32%) and the lowest improvement over north-central India (~ 8%). The reduction of anomalously strong northeasterly flow over the southeastern Arabian Sea and strengthening of the moisture leaden southeasterly wind flow from the Bay of Bengal in Reg25 compared to Reg50 is consistent with the reduction of dry bias over India in Reg25. The robust improvements are noticed for the heavy precipitation events (probability density function: PDF tails) and mean precipitation due to extreme precipitation events, particularly over the areas characterized by complex topographical features (e.g., the Western Ghats, Indo-Gangetic plains, and northeast India and Hilly regions) as well as over the areas having substantial bias (e.g., central India), indicating its strong sensitivity towards model resolution. The increasing latent heat flux in Reg25 contributes to increasing the moisture and hence rainfall over India. Both simulations apparently simulate many of the ISM characteristics better than the EIN, thereby emphasizing the usefulness of finer resolutions in the better simulation of the Indian monsoon, especially for heavy rainfall. However, the RegCM bias is comparable to or even greater in some places than the EIN bias. This suggests that high-resolution models are important for improving performance; however, it does not necessarily mean that they can have AV for every aspect and all places. Apart from this, the substantial difference in the AV over different regions or aspects highlights the importance of carefully selecting AV matrices for the different areas and characteristics being investigated. RegCM exhibits some systematic biases in precipitation despite substantial improvement due to misrepresentation of dynamical and thermodynamical processes, including northward and eastward propagating convective bands. [ABSTRACT FROM AUTHOR]

Intensification of hydrologic cycle, and consequence rise of intense short-term precipitation, are considered as the manifestations of climate change. This may lead to an alteration in Intensity–Duration–Frequency (IDF) relationship that may change other hydrological processes as well. The IDF relationship also serves as a crucial information for the design of any water infrastructure. This study investigates the spatiotemporal changes in IDF relationship involving hourly precipitation events between past and future climate at various return periods across India that spans over a wide range of climatology. Contrast between historical (1979–2014), using two reanalysis data, and future periods (immediate future: 2015–2039, near-future: 2040–2059 and far-future: 2060–2100) is explored along with its spatial (re-) distribution. The future simulations of precipitation are derived from three climate models, participating in 6th phase of Coupled Model Intercomparison Project (CMIP6), for three shared socio-economic pathways (SSPs), i.e., SSP126, SSP245 and SSP585. The results show that almost entire Indian mainland will experience an increase (~41–44%) in the hourly precipitation intensity under the worst climate change scenario (SSP585) with a return period as low as 2 years (almost a regular incidence). Furthermore, even under a moderate climate change scenario (SSP245), almost entire Indian mainland (~82–99% of spatial extent) will be affected from a significant increase (on an average 19%) in the hourly precipitation intensity. It is true for higher return periods as well. Findings of the study are alarming for many water infrastructures. This study develops new set of IDF curves across India considering a changing climate that will be useful to set a revised design criteria for water infrastructure. [ABSTRACT FROM AUTHOR]

Lightning is an electrical discharge — a 'spark' or 'flash' as charged regions in the atmosphere instantly balance themselves through this discharge. It is a beautiful and deadly naturally occurring phenomenon. In June 2020, more than a hundred people died in the state Bihar of India only in 3 days' span due to lightning events. In this study, Lightning Imaging Sensor (LIS) from the Tropical Rainfall Measuring Mission (TRMM) satellite with a very high spatial resolution of 0.1° has been utilized to create the lightning climatology of India for 16 years during the period 1998 to 2013. Diurnal, monthly, and seasonal variations in the occurrence of lightning flash rate density have also been analyzed. TRMM satellite Low-Resolution Monthly Time Series (LRMTS) with 2.5° resolution datasets have been used for lightning trend analysis. The diurnal lightning event mainly occurs in the afternoon/evening time duration (1400–1900 h) around 0.001 flashes/km2/h. The highest lightning occurred in the month of May (0.04 flashes/km2/day) and the least in December (0.005 flashes/km2/day). The distribution of lightning flash counts by season over India landmass is mainly in pre-monsoon months (MAM) which ranges from 0.248 to 0.491 flashes/km2/day, and monsoon (JJA) ranges from 0.284 to 0.451 flashes/km2/day and decreases afterward. Spatially, the distribution of lightning flash density is seen over the North-Eastern region along with Bangladesh, Bihar, Jharkhand, Orissa, and Jammu and Kashmir region. The pre-monsoon (MAM) season shows a positive trend of lightning around 0.04 flashes in the North-Eastern region and the post-monsoon (SON) shows a negative trend of − 0.021 in central India. The CAPE and K-Index have positively correlated with the flash rate density seasonally but CAPE is more significantly correlated around 0.83 during pre-monsoon and monsoon season. This study also focused on finding out the district wise lightning hotspots regions of India. Rajouri and Reasi district in Jammu and Kashmir has the highest lightning with 121 and 115 flashes/km2/yr, respectively. [ABSTRACT FROM AUTHOR]

The variability in duration of withdrawal phase of southwest monsoon over India is often witnessed to influence the northeast monsoon rainfall of India as withdrawal phase is confined between the cessation of southwest monsoon and onset of northeast monsoon. The influence of withdrawal process of southwest monsoon on the variability in southwest monsoon rainfall has been well discussed; however, its impact on the variability in northeast monsoon rainfall is yet to explore. The present investigation is carried out from 1986 to 2016 to identify the important changes associated with withdrawal phase of southwest monsoon and the related variability in northeast monsoon rainfall. Different withdrawal events from 1986 to 2016 is thus divided into three phases: long withdrawal phase (LWP) (> 40 days), medium withdrawal phase (MWP) (30–40 days) and short withdrawal phase (SWP) (20–30 days). It is observed from the study that factors responsible for the variable withdrawal phase of southwest monsoon shifts more towards south/southeast directions during SWP compared to LWP. LWP is accompanied with low rainfall activity along with short length of rainy season over southern peninsular India during the northeast monsoon. Conversely, a reverse result is observed in the case of SWP. The result further shows that LWP and SWP are associated with opposite discernible features during the north–south movement of the monsoon system; however, MWP acts as a mediator and may be considered as standard or normal withdrawal phase of southwest monsoon based on 30 years (1986–2016) climatology. Early/delayed withdrawal of southwest monsoon has no association with the duration of withdrawal phase. [ABSTRACT FROM AUTHOR]

High-resolution regional climate model (RCM) simulations are found to be very useful in deriving realistic climate change projection information. This study uses high-resolution dynamical downscaling framework (CCSM4-WRF) for India. To delineate the advantage of high resolution, we compared the results of 9-km resolution CCSM4-WRF simulations against the 50-km resolution RCM simulations under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) program. Quantitative estimations show that majority of CORDEX-SA models exhibit large dry bias (< − 4 mm/day) and low pattern correlation coefficient (PCC) over the Western Ghats (WG). Mean climatology of Indian summer monsoon (ISM) rainfall simulated by high-resolution CCSM4-WRF outperforms the CORDEX-SA RCMs with low negative biases (~ 1 mm/day) and high PCC (≥ 0.755). This skill of CCSM4-WRF provides better confidence in its future projection at local scale. CCSM4-WRF projects future intensification of monsoon rainfall over most parts of India and reduction over southern WG, which is consistent with recent observed trends, but none of the CORDEX-SA RCMs could simulate this rainfall reduction. For all-India rainfall, ensemble mean of CORDEX-SA models projects an increase by 1.3 ± 0.9 mm/day and CCSM4-WRF projects 0.67 mm/day. Projected changes in socioeconomic variables such as population and gross domestic product (GDP) exhibit future enhancement over most parts of India but with spatial heterogeneity. Shared socioeconomic pathways scenarios show pronounced future population growth over Indian coastal areas and large enhancement in productivity over urban areas. Therefore, climate change projection information of ISM rainfall, together with enhanced future population and GDP, is useful for taking necessary steps for adaptation and mitigation in a sustainable manner. [ABSTRACT FROM AUTHOR]

Amongst all the anthropogenically produced greenhouse gases (GHGs), carbon dioxide (CO2) and methane (CH4) are the most important, owing to their maximum contribution to the net radiative forcing of the Earth. India is undergoing rapid economic development, where fossil fuel emissions have increased drastically in the last three decades. Apart from the anthropogenic activities, the GHGs dynamics in India are governed by the biospheric process and monsoon circulation; however, these aspects are not well addressed yet. Towards this, we have measured CO2 and CH4 concentration at Sinhagad, located on the Western Ghats in peninsular India. The average concentrations of CO2 and CH4 observed during the study period are 406.05 ± 6.36 and 1.97 ± 0.07 ppm (µ ± 1σ), respectively. They also exhibit significant seasonal variabilities at this site. CH4 (CO2) attains its minimum concentration during monsoon (post-monsoon), whereas CO2 (CH4) reaches its maximum concentration during pre-monsoon (post-monsoon). CO2 poses significant diurnal variations in monsoon and post-monsoon. However, CH4 exhibits a dual-peak like pattern in pre-monsoon. The study suggests that the GHG dynamics in the western region of India are significantly influenced by monsoon circulation, especially during the summer season. [ABSTRACT FROM AUTHOR]

The article was published without the Reference section. [ABSTRACT FROM AUTHOR]

Micro-scale perspectives are seldom included in planned climate change adaptations, yet farmers' perceptions can provide useful insights into livelihood impacts from interactions between climatic and other stressors. This research aims to understand how climate variability and other stressors are impacting the livelihoods of smallholder farmers in Azamgarh district, eastern Uttar Pradesh, India. Data from 84 smallholder farmers were collected using mixed qualitative and quantitative approaches, including interview and participatory methods, informed by multiple stressor and sustainable livelihood frameworks. Results revealed that farmers are increasingly facing problems caused by the reduced duration and number of rainy days, and erratic rainfall. Anomalies in seasonal cycles (longer summers, shorter winters) seem to have altered the local climate. Farmers reported that repeated drought impacts, even in years of moderate rainfall, are adversely affecting the rice crop, challenging the formal definition of drought. Climate variability, identified as the foremost stressor, often acts as a risk multiplier for ecological (e.g., soil sodicity), socio-economic (e.g., rising costs of cultivation) and political (e.g., mismatching policies and poor extension systems) stressors. In addition to climate stresses, resource-poor marginal groups in particular experienced higher risks resulting from changes in resource management regimes. This study provides an important cue to revisit the formal definitions of normal rainfall and drought, accommodating farmers' perceptions that evenly distributed rainfall, and not total rainfall is a key determinant of crop yields. Though India has developed adaptive measures for climate change and variability, integration of farmers' perceptions of climate and other stressors into such policies can improve the resilience of smallholder farmers, who have hitherto depended largely on autonomous adaptation strategies. [ABSTRACT FROM AUTHOR]

The spawning and well-being of fish in an ecosystem are closely linked to climatic cues, viz., temperature and rainfall. Reduced fitness can affect the reproductive performance and lead to skipped spawning. Benchmarking the threshold fitness required for a fish population to achieve readiness for spawning, and understanding how climatic parameters influence the fitness will aid in predicting the fate of its reproductive success in future climatic conditions. This study determined the threshold condition factor pre-spawning fitness (Kspawn50) at which 50% of the female Channa punctata population can be deemed fit for spawning. The optimal climate within which pre-spawning fitness is attained by this species under Indian climatic conditions was also identified. The study was conducted from June 2015 to September 2016, covering two spawning seasons (June–August) in a Gangetic floodplain wetland of West Bengal, India. The non-parametric Kaplan–Meier method (survival fit) was used for estimation of pre-spawning fitness. "Ready to spawn" females were classified based on binary coding of the gonadal maturity stages. The thermal and precipitation range within which spawning fitness is achieved was identified by using the locally weighted smoothing technique. Female C. punctata pre-spawning fitness (Kspawn50) ranged from 1.26 to 1.39 with an estimated median of 1.29 units. Temperatures between 29 and 32 °C and rainfall above 100 mm were conducive to attaining the requisite pre-spawning fitness in C. punctata. This is the first study benchmarking the pre-spawning fitness and optimal climate for C. punctata. Understanding spawning requirements can inform the climate change–induced impacts on reproductive plasticity and evolutionary adaptations of snakeheads in the Ganga river basin. [ABSTRACT FROM AUTHOR]

Temperature-depth profiles of boreholes of a region can be analysed to estimate the past variations in climate. Perturbations over the surface ground temperature are imparted deep into the Earth's subsurface. Western Ghats regions of India are considered to be one of the most ecologically rich regions and also play a crucial role in deciding the climatological characteristics of the country. This study quantifies the possible climate warming or cooling in the past century in the Western Ghats using temperature-depth profiles of five boreholes in a depth range of 140–198 m located in Koyna region (17.38° N, 73.74° E) of Maharashtra state, India. The analysis shows that there has been a significant warming of 0.8 ± 0.2 °C over the past 100 years in the region. The inferred warming is comparable with the trends of meteorological records of the area which yields an increasing trend of 0.56 °C in the surface air temperature (SAT). For further validation of results, a combined study of borehole temperature profiles and surface air temperature (SAT) profiles was done which showed that the period was warmer than the earlier long-term temperature records of the region by 0.8 °C. This pre-observational mean (POM) value seems to be consistent with previously studied POMs over India. This study provides a 'true estimate' of climate change and can be used as a robust technique to reconstruct the past climate warming. [ABSTRACT FROM AUTHOR]

The last millennium climate reconstructions are complex and limit our understanding of the mechanisms behind environmental and climate variability. We present multi-proxy centennial-scale records from the Cherai, southwest India. The last 2000 cal yr AD record suggests a complex environmental condition that prevailed at the depositional site augmenting the role of natural as well as anthropogenic agents. Increased elemental variations and indices values indicate stronger weathering, presumably wetter conditions and intense precipitation. Provenance studies suggest diverse sources and the main composition fall close to the Charnockite and Gneissic composition. Multi-proxy data suggests that a shift towards wetter climatic conditions, which occurred from 910 to 1230 cal yr AD. The core also records a shift towards the drier conditions that started around 1230 cal yr AD with a loss in vegetation diversity. The pollution load index values suggest that the overall study area falls in moderate contamination levels, which are also substantiated with the diatom data indicating human influence in the natural habitat during the deposition time. The present study reveals that the enhanced Cd and As concentration is due to strong anthropogenic influence. We compared the multi-proxy record with other continental and marine palaeoclimatic records to explore global and/or regional trends in climate variability during the last 2000 years. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices due to the consideration of difference between precipitation (P) and potential evapotranspiration (PET), which represents the energy-based climatic water balance. Implementation of actual evapotranspiration (AET), which accounts for both water and energy-based climatic evaporative demand in drought characterization studies, is limited. This study proposes a meteorological drought index with the structure of the SPEI and actual evapotranspiration modeled with empirical formulations and remote sensing data integrated with surface energy models at annual scale. The proposed drought index imposes the effect of precipitation, PET, and AET using operational meteorological data sets of precipitation and temperatures. The present study aimed to test how a drought index based on PET and P can outperform with the inclusion of AET at a river basin scale at 12-month scale. The proposed hypothesis was tested considering Krishna River basin, India, as a case study for which most of the basin is in arid climate. The performance of drought indices was compared using historical droughts in terms of severity, areal extent, frequency, and duration based on empirical AET models along with satellite-based land surface ET data-based drought indices. The proposed AET-based drought indices have effectively captured the historical drought years over the Krishna River basin. The empirical AET formulation-based drought index was identified as a more reliable measure in the estimation of drought characteristics by comparing with satellite-based land surface AET-based drought index. The AET-based drought indices were able to drive the areas into moderate, which or otherwise categorized under severe drought regions with PET-based drought indices. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events determined by SPEI into moderate and less frequent droughts with short durations over a large river basin with arid climate. [ABSTRACT FROM AUTHOR]

Precipitation during winter months of December, January, and February over northwest India is important for the production of winter crops as well as for glacial accumulation which goes on to feed major rivers in India with fresh water supply. Winter precipitation over this region is due to the movement of synoptic systems approaching from the Mediterranean known as Western Disturbances (WDs). So far, spatial pattern of winter precipitation over this region has not been studied using different RCMs. The present study evaluates the regional climate model employed in Coordinated Regional Climate Downscaling Experiment in South Asia (CORDEX-South Asia) framework to characterize winter precipitation over northwest India and Karakoram region. There are 16 CORDEX-South Asia experiments with 3 regional climate models (RCM) driven with 16 global climate models (GCM). Precipitation climatology is calculated for different RCM along with their ensemble. All the experiments show a wet bias over the region. The selection of best performing RCM is identified using various statistical analyses. The CORDEX experiment run by EC_EARTH RCM with RCA4 GCM developed by Swedish Meteorological and Hydrological Institute (SMHI) is identified as the best performing model when compared with observation. The change in future seasonal mean precipitation is assessed over northwest India and Karakoram region under two representation concentration pathways (RCP4.5 and RCP8.5) scenarios. A positive change in precipitation is observed over Karakoram region under the two RCP scenarios for near future and far future periods. The magnitude of precipitation change is found to be less over the study area under RCP 8.5 scenario for near future period compared to other scenario and time period. [ABSTRACT FROM AUTHOR]

There is rising interest among research and development practitioners to arrive at impact driven solutions in the field of gender and climate change adaptation. Climate change adaptation interventions can be better targeted by being linked with type of climatic risks experienced by women farmers, their social profile and their needs based on the role they play in agriculture. This study presents a methodology to identify hotspots where climate change adaptation and gender based interventions could be prioritized. The methodology is illustrated for India. The results suggest 36 hotspots across 10 states in India, where large number of women farmers are impacted by high levels of drought probability, excess rainfall and heat wave. The target population in these hotspots comprise 14.4% of the total women farmers in the country. A socioeconomic characterization of the hotspot population highlights barriers, such as labor, credit and market access for female cultivators and lower wage rates for female laborers in these hotspots. Based on the constraints as well as the climatic risks faced by these women in the hotspots, the potential of climate-smart agriculture technologies and practices are emphasized. Additionally, a comparison of current research being done in the field with the results of the study highlights the potential to learn from current efforts for efficient scalability of gender and climate change adaptation interventions. [ABSTRACT FROM AUTHOR]

Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Niño 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2–8 years) and interdecadal (8–32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Niño 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region. [ABSTRACT FROM AUTHOR]

Plant invasion is highly responsive to rising temperature, altered precipitation and various anthropogenic disturbances. Therefore, climate anomalies might provide opportunities to identify the relationship of past climate in deriving the distribution of invasive species and to detect their probable future distribution. In this work, we studied the correlation of climate anomaly i.e. temperature and precipitation with an indicative map of plant invasive species (1° grid) of India. The indicative map was generated through the plant data available from the project 'Biodiversity Characterization at Landscape Level'. Climate anomaly was calculated and represented by average temperature and precipitation using 'Climate Research Unit' data for the period of 1901 to 2000. A comparison of local geographically weighted regression (GWR) model and a global ordinary least square regression (OLS) model was carried out for statistical analysis to depict the correlation at 1° spatial grids. Overall, 20,501 records with a total of 9112 unique plots and 161 unique invasive species were recorded in the database that shows a maximum of 15 invasive species in a 0.04 ha nested quadrat. Cumulative analysis showed a maximum of 53 invasive species at 1° grid. Individually, GWR could reveal a significant correlation with invasive species distribution for temperature anomaly (r2 = 0.73, AIC= 2206) and precipitation anomaly (r2 = 0.74, AIC= 2221), while OLS model did not offer a good correlation (r2 < 0.001, AIC > 2400) compared to GWR. Combination of temperature and precipitation anomaly (shared model) showed an improved spatial correlation (r2 > 0.75) using GWR. Variation partitioning revealed the dominant influence (> 0.40 of variation) of temperature anomaly over Deccan Peninsula, Himalaya and North East zone. Influence of precipitation anomaly was more prominent over arid and semi-arid zone explaining > 0.35 of variation. Results revealed the strength of GWR to see the interaction of invasive plant species w.r.t. climate anomalies that explain the influence of spatial variation due to heterogeneity at varying neighbour distances. The significant correlation of invasive species with both the anomalies revealed the affinity of invasive species towards warmer, drier and wet places. This gives an indication that the distribution of invasive species could be triggered by climate anomaly. The use of other predictor variables (i.e. edaphic and anthropogenic) could be an inclusive input in a future perspective. [ABSTRACT FROM AUTHOR]

In this study, a quantitative assessment of observed aridity variations over the semiarid regions of India is performed for the period 1951–2005 using a dimensionless ratio of annual precipitation (P) and potential evapotranspiration (PET), estimated from five different observed gridded precipitation data sets. The climatological values and changes of this aridity index are found to be sensitive to the choice of the precipitation observations. An assessment of P/PET estimated using the ensemble mean precipitation shows an increase in aridity over several semiarid regions of India, despite the sensitivity of P/PET variations across individual precipitation data sets. Our results indicate that precipitation variations over the semiarid regions of India are outpacing the changes in potential evapotranspiration and, thereby, influencing aridity changes in a significant manner. Our results further reveal a 10% expansion in the area of the semiarid regions during recent decades relative to previous decades, thus highlighting the need for better adaptation strategies and mitigation planning for the semiarid regions in India. The sensitivity of aridity index to multiple PET data sets can be an additional source of uncertainty and will be addressed in a future study. [ABSTRACT FROM AUTHOR]

The present study explores the properties of precipitating shallow echoes (PSEs) over the tropical areas (30°S-30°N) during Indian summer monsoon season using attenuated corrected radar reflectivity factor (Ze) measured by the Tropical Rainfall Measuring Mission satellite. Radar echoes observed in study are less than the freezing height, so they belong to warm precipitation. Radar echoes with at least 0.75 km wide are considered for finding the shallow echoes climatology. Western Ghats and adjoining ocean (Arabian sea) have the highest PSEs followed by Myanmar and Burma coast, whereas the overall west coast of Latin America consists of the lowest PSEs. Tropical oceanic areas contain fewer PSEs compared to coastal areas. Average vertical profiles show nearly similar Ze characteristics which peaks between 1.5 and 2 km altitude with model value 32-34 dBZ. Slope of Ze is higher for intense PSEs as radar reflectivity decreases more rapidly in intense PSEs. [ABSTRACT FROM AUTHOR]

This study uses the global forecast system (GFS) model at T126 horizontal resolution to carry out seasonal simulations with prescribed sea-surface temperatures. Main objectives of the study are to evaluate the simulated Indian monsoon variability in intraseasonal timescales. The GFS model has been integrated for 29 monsoon seasons with 15 member ensembles forced with observed sea-surface temperatures (SSTs) and additional 16-member ensemble runs have been carried out using climatological SSTs. Northward propagation of intraseasonal rainfall anomalies over the Indian region from the model simulations has been examined. It is found that the model is unable to simulate the observed moisture pattern when the active zone of convection is over central India. However, the model simulates the observed pattern of specific humidity during the life cycle of northward propagation on day − 10 and day + 10 of maximum convection over central India. The space-time spectral analysis of the simulated equatorial waves shows that the ensemble members have varying amount of power in each band of wavenumbers and frequencies. However, variations among ensemble members are more in the antisymmetric component of westward moving waves and maximum difference in power is seen in the 8-20 day mode among ensemble members. [ABSTRACT FROM AUTHOR]

This study assess the performance of two versions of Regional Climate Model (RegCM) in simulating the Indian summer monsoon over South Asia for the period 1998 to 2003 with an aim of conducting future climate change simulations. Two sets of experiments were carried out with two different versions of RegCM (viz. RegCM4.2 and RegCM4.3) with the lateral boundary forcings provided from European Center for Medium Range Weather Forecast Reanalysis (ERA-interim) at 50 km horizontal resolution. The major updates in RegCM4.3 in comparison to the older version RegCM4.2 are the inclusion of measured solar irradiance in place of hardcoded solar constant and additional layers in the stratosphere. The analysis shows that the Indian summer monsoon rainfall, moisture flux and surface net downward shortwave flux are better represented in RegCM4.3 than that in the RegCM4.2 simulations. Excessive moisture flux in the RegCM4.2 simulation over the northern Arabian Sea and Peninsular India resulted in an overestimation of rainfall over the Western Ghats, Peninsular region as a result of which the all India rainfall has been overestimated. RegCM4.3 has performed well over India as a whole as well as its four rainfall homogenous zones in reproducing the mean monsoon rainfall and inter-annual variation of rainfall. Further, the monsoon onset, low-level Somali Jet and the upper level tropical easterly jet are better represented in the RegCM4.3 than RegCM4.2. Thus, RegCM4.3 has performed better in simulating the mean summer monsoon circulation over the South Asia. Hence, RegCM4.3 may be used to study the future climate change over the South Asia. [ABSTRACT FROM AUTHOR]

This work is the first attempt to produce a multi-year downscaled regional reanalysis of the Indian summer monsoon (ISM) using the National Centers for Environmental Prediction (NCEP) operational analyses and Atmospheric Infrared Sounder (AIRS) version 5 temperature and moisture retrievals in a regional model. Reanalysis of nine monsoon seasons (2003-2011) are produced in two parallel setups. The first set of experiments simply downscale the original NCEP operational analyses, whilst the second one assimilates the AIRS temperature and moisture profiles. The results show better representation of the key monsoon features such as low level jet, tropical easterly jet, subtropical westerly jet, monsoon trough and the spatial pattern of precipitation when AIRS profiles are assimilated (compared to those without AIRS data assimilation). The distribution of temperature, moisture and meridional gradients of dynamical and thermodynamical fields over the monsoon region are better represented in the reanalysis that assimilates AIRS profiles. The change induced by AIRS data on the moist and thermodynamic conditions results in more realistic rendering of the vertical shear associated with the monsoon, which in turn leads to a proper moisture transport and the moist convective feedback. This feedback benefits the representation of the regional monsoon characteristics, the monsoon dynamics and the moist convective processes on the seasonal time scale. This study emphasizes the use of AIRS soundings for downscaling of ISM representation in a regional reanalysis. [ABSTRACT FROM AUTHOR]

Southeast India experienced a heavy rainfall during 30 Nov-2 Dec 2015. Particularly, the Chennai city, the fourth major metropolitan city in India with a population of 5 million, experienced extreme flooding and causalities. Using various observed/reanalysed datasets, we find that the concurrent southern Bay of Bengal (BoB) sea surface temperatures (SST) were anomalously warm. Our analysis shows that BoB sea surface temperature anomalies (SSTA) are indeed positively, and significantly, correlated with the northeastern Indian monsoonal rainfall during this season. Our sensitivity experiments carried out with the Weather Research and Forecasting (WRF) model at 25 km resolution suggest that, while the strong concurrent El Niño conditions contributed to about 21.5% of the intensity of the extreme Chennai rainfall through its signals in the local SST mentioned above, the warming trend in BoB SST also contributed equally to the extremity of the event. Further, the El Niño southern oscillation (ENSO) impacts on the intensity of the synoptic events in the BoB during the northeast monsoon are manifested largely through the local SST in the BoB as compared through its signature in the atmospheric circulations over the BoB. [ABSTRACT FROM AUTHOR]

The present research aimed at analyzing temporal trends in thermal discomfort indices for a period of 46 years from 1969 to 2014 over western coastal region of India for seven urban centers during the months of pre-monsoon and monsoon seasons. Direct thermal discomfort indices employed for this purpose were thermo-hygrometric index (THI) and heat index (HI). Statistical techniques applied for obtaining temporal trends were linear regression model and Mann-Kendall (MK) rank test. Statistical significance of the obtained trends was evaluated at 95% confidence level. Sequential MK (SQ-MK) test was used for change point detection. To investigate actual incidences of thermal discomfort, daily index values were averaged for standard meteorological weeks (SMWs) over the study period and decadal percentage of thermal discomfort during SMWs was estimated. Trend analysis of selected meteorological parameters such as dry bulb temperature (DBT), wet bulb temperature (WBT), relative humidity (RH), and wind speed (WS) were investigated, which might be responsible for variation in thermal discomfort over the period. The results obtained depicted significant increase in thermal discomfort over the cities located on the southern part of west coast, while significant increase was observed during monsoon season months compared to pre-monsoon season. Decadal variation in percentage of SMWs falling in various discomfort categories was studied. At majority of the stations, moderate and high-risk SMWs have increased over the last two decades. The results of change point detection for THI and HI denoted significant increase at most of the stations after 1990s. The study validates increase in thermal discomfort vulnerability, particularly at thriving urban centers of western coastal region of India. [ABSTRACT FROM AUTHOR]

Meteorological drought during the southwest monsoon season and for the northeast monsoon season over five meteorological subdivisions of India for the period 1901-2015 has been examined using district and all India standardized precipitation index (SPI). Whenever all India southwest monsoon rainfall was less than −10% or below normal, for those years all India SPI was found as −1 or less. Composite analysis of SPI for the below normal years, viz., less than −15% and −20% of normal rainfall years indicate that during those years more than 30% of country's area was under drought condition, whenever all India southwest monsoon rainfall was -15% or less than normal. Trend analysis of monthly SPI for the monsoon months identified the districts experiencing significant increase in drought occurrences. Significant positive correlation has been found with the meteorological drought over most of the districts of central, northern and peninsular India, while negative correlation was seen over the districts of eastern India with NINO 3.4 SST. For the first time, meteorological drought analysis over districts and its association with equatorial pacific SST and probability analysis has been done for the northeast monsoon over the affected regions of south peninsular India. Temporal correlation of all India southwest monsoon SPI and south peninsular India northeast monsoon SPI has been done with the global SST to identify the teleconnection of drought in India with global parameters. [ABSTRACT FROM AUTHOR]

Inter-annual variability in the onset of monsoon over Kerala (MOK), India, is investigated using daily temperature; mean sea level pressure; winds at 850, 500 and 200 hPa pressure levels; outgoing longwave radiation (OLR); sea surface temperature (SST) and vertically integrated moisture content anomaly with 32 years (1981-2013) observation. The MOK is classified as early, delayed, or normal by considering the mean monsoon onset date over Kerala to be the 1st of June with a standard deviation of 8 days. The objective of the study is to identify the synoptic setup during MOK and comparison with climatology to estimate the predictability of the onset type (early, normal, or delayed) with 5, 10, and 15 days lead time. The study reveals that an enhanced convection observed over the Bay of Bengal during early MOK is found to shift over the Arabian Sea during delayed MOK. An intense high-pressure zone observed over the western south Indian Ocean during early MOK shifts to the east during delayed MOK. Higher tropospheric temperature (TT) over the western Equatorial Ocean during early MOK and lower TT over the Indian subcontinent intensify the land-ocean thermal contrast that leads to early MOK. The sea surface temperature (SST) over the Arabian Sea is observed to be warmer during delayed than early MOK. During early MOK, the source of 850 hPa southwesterly wind shifts to the west equatorial zone while a COL region has been found during delayed MOK at that level. The study further reveals that the wind speed anomaly at the 200-hPa pressure level coincides inversely with the anomaly of tropospheric temperature. [ABSTRACT FROM AUTHOR]

Thirty-six Coupled Model Intercomparison Project-5-based global climate models (GCMs) are explored to evaluate the performance of maximum ( T ) and minimum ( T ) temperature simulations for India covering 40 grid points. Three performance indicators used for evaluating GCMs are correlation coefficient (CC), normalised root mean square error (NRMSE) and skill score (SS). Entropy method is applied to compute the weights of the three indicators employed. However, equal weights are also considered as part of sensitivity analysis studies. Compromise programming (CP), a distance-based decision-making technique, is employed to rank the GCMs. Group decision-making approach is used to aggregate the ranking patterns obtained for individual grid points. A simple but effective ensemble approach is also suggested. [ABSTRACT FROM AUTHOR]

In this study, 43-year (1965-2007) monthly and annual rainfall time series of ten rainfall stations in a semi-arid region of western India are analyzed by adopting three tests for testing normality and by applying autoregressive technique for exploring persistence. Gradual trends are identified by three tests, and their magnitudes are assessed by the Sen's slope estimator. Also, abrupt changes are detected by using four tests and they are further confirmed by two tests. Box-whisker plots revealed that the rainfalls of June and September are right skewed for all the stations. The annual rainfalls of Bhinder, Dhariawad, and Gogunda stations are found considerably right skewed. The normality tests indicated that the rainfall of July does not deviate from the normal distribution at all the stations. However, the annual rainfall is found non-normal at five stations. The monthly rainfalls of June, July, and August have persistence respectively at three (Mavli, Salumber, and Sarada), two (Kherwara and Sarada), and one (Mavli) stations, whereas the annual rainfall has persistence at Girwa and Mavli stations. Significantly increasing trend is detected at Mavli in the rainfall of July and in the annual rainfall ( p value > 0.05), while the negative trend in August rainfall at Dhariawad is found significant ( p value > 0.10). This study revealed that the presence of serial correlation does not affect the performance of the Mann-Kendall test. Mean values of trend magnitudes for the rainfalls of June, July, August, and September are 0.3, 0.8, −0.4, and 0.4 mm year, respectively, and the overall mean value for the annual rainfall is 0.9 mm year. It is found that the standard normal homogeneity test and the Pettitt test are biased towards the end of the series to locate a change point. Conversely, the Bayesian test has a tendency to look for a change point in the beginning of time series. Confirmed abrupt changes in the rainfall time series are found in the year 2003 (Bhinder) in June; years 1974 (Mavli) and 1989 (Dhariawad and Salumber) in July; years 1972 (Sarada), 1990 (Dhariawad), and 2003 (Mavli) in August; years 1977 (Dhariawad), 1991 (Sarada), and 2004 (Kotra) in September; and in the year 1972 (Mavli and Sarada stations) in the annual series. It is emphasized that the significantly increasing trend of rainfall may have linkages with climate change and/or variability. Finally, this study recommends use of multiple statistical tests for analyzing hydrologic time series in order to ensure reliable decisions. [ABSTRACT FROM AUTHOR]

Intra-seasonal and inter-annual variability of Bowen Ratio (BR) have been studied over the rain-shadow region of north peninsular India during summer monsoon season. Daily grid point data of latent heat flux (LHF), sensible heat flux (SHF) from NCEP/NCAR Reanalysis for the period 1970-2014 have been used to compute daily area-mean BR. Daily grid point rainfall data at a resolution of 0.25° × 0.25° from APHRODITE's Water Resources for the available period 1970-2007 have been used to study the association between rainfall and BR. The study revealed that BR rapidly decreases from 4.1 to 0.29 in the month of June and then remains nearly constant at the same value (≤0.1) in the rest of the season. High values of BR in the first half of June are indicative of intense thermals and convective clouds with higher bases. Low values of BR from July to September period are indicative of weak thermals and convective clouds with lower bases. Intra-seasonal and inter-annual variability of BR is found to be inversely related to precipitation over the region. BR analysis indicates that the land surface characteristics of the study region during July-September are similar to that over oceanic regions as far as intensity of thermals and associated cloud microphysical properties are concerned. Similar variation of BR is found in El Nino and La Nina years. During June, an increasing trend is observed in SHF and BR and decreasing trend in LHF from 1976 to 2014. Increasing trend in the SHF is statistically significant. [ABSTRACT FROM AUTHOR]

The Indian and East Asian summer monsoons are two components of the whole Asian summer monsoon system. Previous studies have indicated in-phase and out-of-phase variations between Indian and East Asian summer rainfall. The present study reviews the current understanding of the connection between Indian and East Asian summer rainfall. The review covers the relationship of northern China, southern Japan, and South Korean summer rainfall with Indian summer rainfall; the atmospheric circulation anomalies connecting Indian and East Asian summer rainfall variations; the long-term change in the connection between Indian and northern China rainfall and the plausible reasons for the change; and the influence of ENSO on the relationship between Indian and East Asian summer rainfall and its change. While much progress has been made about the relationship between Indian and East Asian summer rainfall variations, there are several remaining issues that need investigation. These include the processes involved in the connection between Indian and East Asian summer rainfall, the non-stationarity of the connection and the plausible reasons, the influences of ENSO on the relationship, the performance of climate models in simulating the relationship between Indian and East Asian summer rainfall, and the relationship between Indian and East Asian rainfall intraseasonal fluctuations. [ABSTRACT FROM AUTHOR]

The regional climate model (RegCM4) is customized for 10-year climate simulation over Indian region through sensitivity studies on cumulus convection and land surface parameterization schemes. The model is configured over 30° E-120° E and 15° S-45° N at 30-km horizontal resolution with 23 vertical levels. Six 10-year (1991-2000) simulations are conducted with the combinations of two land surface schemes (BATS, CLM3.5) and three cumulus convection schemes (Kuo, Grell, MIT). The simulated annual and seasonal climatology of surface temperature and precipitation are compared with CRU observations. The interannual variability of these two parameters is also analyzed. The results indicate that the model simulated climatology is sensitive to the convection as well as land surface parameterization. The analysis of surface temperature (precipitation) climatology indicates that the model with CLM produces warmer (dryer) climatology, particularly over India. The warmer (dryer) climatology is due to the higher sensible heat flux (lower evapotranspiration) in CLM. The model with MIT convection scheme simulated wetter and warmer climatology (higher precipitation and temperature) with smaller Bowen ratio over southern India compared to that with the Grell and Kuo schemes. This indicates that a land surface scheme produces warmer but drier climatology with sensible heating contributing to warming where as a convection scheme warmer but wetter climatology with latent heat contributing to warming. The climatology of surface temperature over India is better simulated by the model with BATS land surface model in combination with MIT convection scheme while the precipitation climatology is better simulated with BATS land surface model in combination with Grell convection scheme. Overall, the modeling system with the combination of Grell convection and BATS land surface scheme provides better climate simulation over the Indian region. [ABSTRACT FROM AUTHOR]

The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water such as 83 % water used for agriculture sector, 12 % for industry sector and only 5 % for domestic sector. In this study, the historical rainfall data for the periods 1901-2002 and 1942-2002 of the Sindh river basin, India, were analysed for monthly, seasonal and annual trends. The conventional Mann-Kendall test (MK) and Mann-Kendall test (MMK), after the removal of the effect of all significant autocorrelation coefficients, and Sen's slope estimator were used to identify the trends. Kriging technique was used for interpolating the spatial pattern using Arc GIS 9.3. The analysis suggested significant increase in the trend of rainfall for seasonal and annual series in the Sindh basin during 1901-2002. [ABSTRACT FROM AUTHOR]

The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December-January-February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982-2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction. [ABSTRACT FROM AUTHOR]

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001-2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3-5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature. [ABSTRACT FROM AUTHOR]

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial-temporal analyses of rainfall have been studied by using 107 (1901-2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region. [ABSTRACT FROM AUTHOR]

In this study, analysis of the long term climatology, variability and trends in the daily rainfall events of ≥5 mm [or daily rainfall (DR) events] during the southwest monsoon season (June-September) over four regions of India; south central India (SCI), north central India (NCI), northeast India (NEI) and west coast (WC) have been presented. For this purpose, a new high spatial resolution (0.25° × 0.25°) daily gridded rainfall data set covering 110 years (1901-2010) over the Indian main land has been used. The association of monsoon low pressure systems (LPSs) with the DR events of various intensities has also been examined. Major portion of the rainfall over these regions during the season was received in the form of medium rainfall (≥5-100 mm) or moderate rainfall (MR) events. The mean seasonal cycle of the daily frequency of heavy rainfall (HR) (≥100-150 mm) or HR events and very heavy rainfall (VHR) (≥150 mm) or VHR events over each of the four regions showed peak at different parts of the season. The peak in the mean daily HR and VHR events occurred during middle of July to middle of August over SCI, during late part of June to early part of July over NCI, during middle of June to early July over NEI, and during late June to middle July over WC. Significant long term trends in the frequency and intensity of the DR events were observed in all the four geographical regions. Whereas the intensity of the DR events over all the four regions showed significant positive trends during the second half and the total period, the signs and magnitude of the long term trends in the frequency of the various categories of DR events during the total period and its two halves differed from the region to the region. The trend analysis revealed increased disaster potential for instant flooding over SCI and NCI during the recent years due to significant increasing trends in the frequency (areal coverage) and intensity of the HR and VHR events during the recent half of the data period. However, there is increased disaster potential over NEI and WC due to the increasing trends in the intensity of the rainfall events. There is strong association between the LPS days and the DR events in both the spatial and temporal scales. In all the four regions, the contributions to the total MR events by the LPS days were nearly equal. On the other hand, there was relatively large regional difference in the number of combined HR and VHR events associated with LPS days particularly that associated with monsoon depression (LPS stronger than monsoon depression) days. The possible reasons for the same have also been discussed. The increasing trend in the monsoon low (low pressure) days post 1970s is the primary reason for the observed significant increasing trends in the HR and VHR events over SCI and NCI and decreasing trend in HR events over NEI during the recent half (1956-2010). This is in spite of the decreasing trend in the MD days. [ABSTRACT FROM AUTHOR]

Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A) and Chi-Square test ( X) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures. [ABSTRACT FROM AUTHOR]

The present study is focused to see the performance of real-time extended range forecast (2-3 week forecast) of monsoon during the 2011 flood event over the eastern state of Odisha in India. The usefulness of such forecast in the management of reservoir flow in a way to reduce the risk of flood conditions is also discussed. This case study is carried out over the Mahanadi River basin situated in eastern region of India, which witnessed severe flood conditions during the late August and early part of September 2011 due to abnormally high rainfall over the Mahanadi River basins leading to simultaneous release of huge volume of water from the Hirakud reservoir. The forecasts are prepared based on the Bi-model average (BMA) of two coupled model outputs viz., the European Centre for Medium-Range Weather Forecasting (ECMWF) monthly forecast model and the National Centre for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The synoptic analysis of the observed rainfall patterns demonstrated that the heavy rainfall is associated with active monsoon circulation during late August and early September, 2011 with the presence of low-pressure systems and anomalous cyclonic circulations at lower level. The extended range BMA forecast based on the initial condition of 25 August, 2011 valid for week 1 (days 5-11) and week 2 (days 12-18) indicate strong monsoon conditions associated with heavy rainfall over the Mahanadi River basins during the period from 29 August to 11 September 2011. With respect to individual model, the ECMWF model is better compared to NCEP CFS in capturing this rainfall episode, although it is much less compared to observed rainfall departure during the period. A mechanism to use such climate forecast along with the use of latest climatological information in the decision making process will be very useful for effective management of reservoir operation particularly during this difficult period. [ABSTRACT FROM AUTHOR]

India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) track forecast valid for next 24 hrs over the North Indian Ocean (NIO) in 2003. It further extended the validity period up to 72 hrs in 2009. Here an attempt is made to evaluate the TC landfall forecast issued by IMD during 2003-2013 (11 years) by calculating the landfall point forecast error (LPE) and landfall time forecast error (LTE). The average LPE is about 67, 95, and 124 km and LTE is about 4, 7, and 2 hrs, respectively for 24, 48, and 72-hr forecasts over the NIO as a whole during 2009-2013. The accuracy of TC landfall forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea, and NIO as a whole), specific regions of landfall, season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm (CS), and severe cyclonic storm (SCS) or higher intensities) at the time of initiation of forecast and type of track of TCs (climatological/straight moving and recurving/looping type). The LPE is less over the BOB than over the AS for all forecast lengths up to 72 hrs. Similarly, the LPE is less during the post-monsoon season than during pre-monsoon season. The LPEs are less for climatologically moving/straight moving TCs than for the recurving/looping TCs. The LPE over the NIO has decreased at the rate of about 14.5 km/year during 2003-2013 for 24-hr forecasts. The LTE does not show any significant improvement for 24-hr forecast during the same period. There is significant decrease in LPE and LTE during 2009-2013 compared to 2003-2008 due to the modernisation programme of IMD. The 24-hr LPE and LTE have decreased from 157.5 to 66.5 km and 7.8 to 4.1 hrs, respectively. However, there is still scope for further reduction in 48 and 72-hr forecast errors over the NIO to about 50 and 100 km respectively based on the latest technology including aircraft reconnaissance, deployment of buoys, and assimilation of more observational data from satellite and Doppler weather radars, etc., in the numerical weather prediction (NWP) models during the next five years. [ABSTRACT FROM AUTHOR]

The ancient civilizations in India, China, Egypt and Mesopotamia have flourished due to large rivers that provided water for agriculture over millennia. Egypt was able to grow well because of the Nile. Similarly, Mesopotamia had two rivers namely the Tigris and the Euphrates. Likewise, India and China have several great rivers that continue to support the agrarian culture. This article discusses the sacred significance of rivers in the ancient and contemporary Indian culture with examples from popular Hindu scriptures. It also presents the ancient model of an eco-friendly check dam and its modern application with potential to mitigate future water-related problems across the drylands of India and elsewhere. [ABSTRACT FROM AUTHOR]

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June-September (JJAS) by using hindcast of the Japan Meteorological Agency (JMA) seasonal ensemble prediction system (EPS) model, based on five ensembles of March, April and May initial states for a period of 32 years (1979-2010). The hindcast climatology during JJAS simulates the mean monsoon circulation at lower and upper tropospheres very well in JMA model using March, April and May ensembles with a more realistic simulation of Webster and Yang's broad scale monsoon circulation index. The JMA hindcast climatology during JJAS simulates the rainfall maxima over the west-coast of India and the head Bay of Bengal reasonably well, although, the latter is slightly shifted southwestward. Associated with better forecast skills of El Nino in the JMA model, the interannual variability of All India Summer Monsoon Rainfall (AISMR) during the 32-year period has also been very well simulated with a high significant (99% level) correlation in April ensemble followed by that of March and May ensembles. Thus, the present analysis indicates that the JMA seasonal forecast model can prove to be a useful tool for the dynamical seasonal forecast of AISMR. [ABSTRACT FROM AUTHOR]

The Asia's largest lagoon, Chilika, is a shallow water estuary and a designated 'Ramsar' site located in the east coast of India. The spatiotemporal diversity of phytoplankton based on the monthly sampling between July 2011 and June 2012 was investigated in relation to physicochemical variables of the surface water column from 13 stations. The salinity was minimum (average 9) during the monsoon which was primarily due to riverine discharge. As the season progressed towards post-monsoon, average salinity of the whole lagoon reached to 10 which further increased to 20 during pre-monsoon season. A total of 259 species of phytoplankton, mostly dominated by the Bacillariophyta (138 species) followed by Dinophyta (38 species), Chlorophyta (32 species), Cyanophyta (29 species), Euglenophyta (18 species), and Chrysophyta (4 species), were recorded in this study. Different ecological sectors of the lagoon (except the northern sector) were dominated by diatoms, while the northern sector due to its freshwater regime supported large population of euglenoids. Based on the multivariate ordination analysis, salinity regime and light availability played important role in determining the distribution, diversity, and composition of phytoplankton communities. Overall, this study documented a very high diversity of phytoplankton and highlighted the importance of taking extensive sampling in getting a clearer understanding of phytoplankton community structure in less-studied environments such as Chilika lagoon. [ABSTRACT FROM AUTHOR]

Analyses of 60 years (1949-2008) of monthly energetics of the zonal waves derived from NCEP/NCAR data indicate that ultra-long waves (waves 1 and 2) dominate the spectrum of lower tropospheric zonal waves during monsoon season (June-September). Westerlies over the Indian subcontinent are a source of energy to wave 1. Two oceanic anticyclones, one over Pacific and the other over Atlantic are sources of energy to wave 2. These two waves are inversely correlated. Climatology of the energetics of ultra-long waves for the two epochs 1949-1978 (CLP1) and 1979-2008 (CLP2) of 30 years indicates that the intensity of wave 1 has decreased by about 33% whereas the intensity of wave 2 has increased by about 27%. Northward transport of sensible heat during CLP1 changes to southward during CLP2. Larger generation of zonal mean Available Potential Energy (APE) during CLP2 indicates more heating. A larger conversion of kinetic energy (KE) of wave 1 into APE of wave 1 leads to weakening of wave 1 during CLP2. In case of wave 2, lower rate of conversion of KE to APE leads to stronger wave 2 during CLP2. [ABSTRACT FROM AUTHOR]

The real-time forecasting of monsoon activity over India on extended range time scale (about 3 weeks) is analyzed for the monsoon season of 2012 during June to September (JJAS) by using the outputs from latest (CFSv2 [Climate Forecast System version 2]) and previous version (CFSv1 [Climate Forecast System version 1]) of NCEP coupled modeling system. The skill of monsoon rainfall forecast is found to be much better in CFSv2 than CFSv1. For the country as a whole the correlation coefficient (CC) between weekly observed and forecast rainfall departure was found to be statistically significant (99 % level) at least for 2 weeks (up to 18 days) and also having positive CC during week 3 (days 19-25) in CFSv2. The other skill scores like the mean absolute error (MAE) and the root mean square error (RMSE) also had better performance in CFSv2 compared to that of CFSv1. Over the four homogeneous regions of India the forecast skill is found to be better in CFSv2 with almost all four regions with CC significant at 95 % level up to 2 weeks, whereas the CFSv1 forecast had significant CC only over northwest India during week 1 (days 5-11) forecast. The improvement in CFSv2 was very prominent over central India and northwest India compared to other two regions. On the meteorological subdivision level (India is divided into 36 meteorological subdivisions) the percentage of correct category forecast was found to be much higher than the climatology normal forecast in CFSv2 as well as in CFSv1, with CFSv2 being 8-10 % higher in the category of correct to partially correct (one category out) forecast compared to that in CFSv1. Thus, it is concluded that the latest version of CFS coupled model has higher skill in predicting Indian monsoon rainfall on extended range time scale up to about 25 days. [ABSTRACT FROM AUTHOR]