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Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach. [ABSTRACT FROM AUTHOR]

Uncertainties in future changes of temperature and precipitation over the homogenous monsoon regions of India are investigated using the CMIP5 and CESM-LE datasets. The uncertainty is partitioned into epistemic (model) and aleatoric (internal variability) components for each season using the RCP8.5 scenario. The uncertainty in temperature change is dominated by epistemic uncertainty that increases over time. The uncertainty in precipitation change shows a more complex picture. Aleatoric uncertainty can remain quite large and comparable to epistemic uncertainty till the latter part of the twenty-first Century especially during the JJA and SON seasons. Much of the rainfall uncertainty is in the more arid Northwest region with the West Central region (part of the core monsoon area) exhibiting lower uncertainties. Considerable increase in rainfall is seen during the SON season indicating an extended monsoon season. During the DJF season aleatoric uncertainty is much larger than epistemic uncertainty over much of the century and shows considerable decadal scale variability. Using the 40-member CESM-LE ensemble to analyze the influence of ensemble size on aleatoric uncertainty we find that low ensemble sizes can lead to an underestimate of the uncertainty. [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]

Tree-ring-width data of Himalayan cedar [ Cedrus deodara (Roxb.) G. Don] from 11 homogeneous moisture stressed sites in the monsoon shadow zone of the western Himalaya were used to develop a mean chronology extending back to ad 1353. The chronology developed using Regional Curve Standardization method is the first from the Himalayan region of India showing centennial-scale variations. The calibration of ring-width chronology with instrumental precipitation data available from stations close to the tree ring sampling sites showed strong, direct relationship with March-April-May-June (MAMJ) precipitation. This strong relationship was used to supplement the instrumental precipitation data back to ad 1410. The precipitation reconstruction showed extended period of drought in fifteenth and sixteenth centuries. Increasingly pluvial conditions were recorded since eighteenth century, with the highest precipitation in the early part of the nineteenth century. The decreasing trend in reconstructed precipitation in the last decade of the twentieth century, consistent with the instrumental records, is associated with the decreasing trend in frequency of western disturbances. MAMJ precipitation over the monsoon shadow zone in the western Himalaya is directly associated with the North Atlantic Oscillation (NAO) and NINO3-SST index of El Nino-Southern Oscillation (ENSO), the leading modes of climate variability influencing climate over large parts of the Northern Hemisphere. However, the relationship between ENSO and MAMJ precipitation collapsed completely during 1930-1960. The breakdown in this relationship is associated with the warm phase of Atlantic Multidecadal Oscillation (AMO). A spectral analysis of reconstructed MAMJ precipitation indicates frequencies in the range of the variability associated with modes of NAO, ENSO and AMO. [ABSTRACT FROM AUTHOR]