The western Himalayan region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902–2005. Annual and seasonal rainfall change over the WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from phase 3 of the Coupled Model Intercomparison Project (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends, and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend, whereas approximately 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30 yr) trend estimates than for the longer-term (99 yr) trends from CMIP5 GCMs. GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in premonsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high-resolution version of the MIROC3.2 model [MIROC3.2 (hires)] and MIROC5 at the top in CMIP3 and CMIP5, respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the models as compared to other methods.

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Assessing warming over the Western Himalayan Region (WHR) of India is challenging due to its limited station data availability and poor data quality. The missing values in the station data were replaced using the Multiple Imputation Chained Equation technique. Finally, 16 stations having continuous records during 1969-2009 were considered as the 'reference stations' for assessing the warming/cooling trends in addition to evaluate the Coupled Model Intercomparison, phase 5 (CMIP5), Global Circulation Model (GCM). Station data indicates winter (DJF) warming is higher and rapid (1.41 ∘ C) than the other seasons and less warming was observed in the post-monsoon (0.31 ∘ C) season. Overall mean annual warming over WHR is ∼0.84 ∘ C during 1969-2009. The performance of 34 CMIP5 models was evaluated based on three different criteria namely (1) mean seasonal cycle, (2) temporal trends and (3) spatial correlation between simulated and observed signals for common available period of 1969-2003 over the study area. Models are provided a final rank on the basis of the cumulative rank obtained in each of three approaches. CMCC-CM, GISS-E2-H and MIROC 5 are three top-ranked models while MIROC-ESM, MIROC-ESM-CHEM and bcc-csm1-1 are three bottom-ranked models over the WHR. The study also extended to judge whether the selected top-ranked models perform well through two alternative data sources namely European Reanalysis (ERA)-interim and Climate Research Unit (CRU), which have not used in the process of model evaluation. The spatial patterns of top-ranked GCM are similar to the spatial pattern obtained through ERA-interim and CRU while zoomed in to WHR but bottom-ranked models fail to reproduce such spatial patterns indicating the top-ranked GCMs would offer more reliability for projecting future climate over WHR.

In the present study we investigate the performance of climate models which contributed to the past 3 Intergovernmental Panel for Climate Change (IPCC) assessment reports for the Gangetic West Bengal region of east India (6° × 6°). Analysing present-day seasonal rainfall and temperature over the domain, we compare the results of the models (from the 6 modelling centres common to the second, third and fourth assessment reports -SAR, TAR and AR4, respectively) in order to judge to what extent these global models have improved on a regional scale. Metrics for model evaluation are not yet firmly established in the literature, so in this paper we compare and contrast the results from a number of different statistics used in previous studies. We also analyse the impact of topography on the results obtained for the AR4 models. We find that most models improved from SAR to AR4, although there is some variation in this result depending on seasons, variables and on which statistical methods are used in the analysis. The multi-model mean of the 6 models improves from SAR to TAR to AR4. The overall best performance in this region in the AR4 is the Japanese model, MIROC, but the best model in terms of improving skill from SAR to AR4 is the GFDL model from the United States. Correcting for errors in the model topographies produced an overall improvement of spatial patterns and error statistics, and greatly improves the performance of 1 model (CGCM) which has poor topography, but does not affect the ranking of the other models.

For centuries arsenic has played an important role in science, technology, and medicine. Arsenic for its environmental pervasiveness has gained unexpected entrance to the human body through food, water and air, thereby posing a great threat to public health due to its toxic effect and carcinogenicity. Thus, in modern scenario arsenic is synonymous with “toxic” and is documented as a paradoxical human carcinogen, although its mechanism of induction of neoplasia remains elusive. To assess the risk from environmental and occupational exposure of arsenic, in vivo cytogenetic assays have been conducted in arseniasis-endemic areas of the world using chromosomal aberrations (CA) and sister chromatid exchanges (SCE) as biomarkers in peripheral blood lymphocytes. The primary aim of this report is to critically review and update the existing in vivo cytogenetic studies performed on arsenic-exposed populations around the world and compare the results on CA and SCE from our own study, conducted in arsenic-endemic villages of North 24 Parganas (district) of West Bengal, India from 1999 to 2003. Based on a structured questionnaire, 165 symptomatic (having arsenic induced skin lesions) subjects were selected as the exposed cases consuming water having a mean arsenic content of 214.96 µg/l. For comparison 155 age-sex matched control subjects from an unaffected district (Midnapur) of West Bengal were recruited. Similar to other arsenic exposed populations our population also showed a significant difference (P < 0.01) in the frequencies of CA and SCE between the cases and control group. Presence of substantial chromosome damage in lymphocytes in the exposed population predicts an increased future carcinogenic risk by this metalloid.

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