This study assesses the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) dataset in representing the Indian summer monsoon (ISM) precipitation over the Himalayan region against ground-based gridded (IMD), satellite-based (TRMM, GPM-IMERG and CHIRPS) and ERA5 reanalysis datasets at seasonal mean, trends, interannual and diurnal timescales. We first evaluate the relative performance of IMDAA rainfall using various statistical performance measures against the aforementioned datasets. Analysis suggests that IMDAA successfully captures the spatial distribution of ISM mean precipitation and seasonal cycle of daily rainfall over the Himalayas as in gridded, satellite and ERA5 reanalysis datasets, albeit with some overestimation in magnitude. Furthermore, statistical results show that IMDAA exhibits a wetter tendency, more RMSE, and higher precipitation variations over the foothills of the Himalayas. The results of the skill score determine that IMDAA diligently captured moderate and extreme precipitation events but was unable to detect heavy and low-precipitation events. Our analysis further reveals that IMDAA shows a positive trend in mean ISM precipitation over the western Himalayan region, in line with CHIRPS, IMERG, IMD, ERA5 and TRMM datasets. Investigation of rainfall trends from station observations over the foothills of the Himalayas revealed mixed trends, with some stations (Tehri, Uttarkashi and Mukhim) showing an increasing trend and others (Dunta and Bhatwari) showing a decreasing trend. However, IMDAA reanalysis agrees well with all gauge station-based trends. Additionally, the interannual variability of Himalayan precipitation is well represented in IMDAA as its principal component correlates well with its seasonal precipitation anomalies. It is found that the leading empirical orthogonal function mode of IMDAA accounts 24% of the total variance, demonstrating a single mode of variability. Furthermore, it is noticeable that strong convective activity during wet ISM years than in dry years, suggesting that the IMDAA can capture these variations successfully compared to ERA5. The interannual variability of ISM precipitation over the Himalayas is strongly associated with the El Niño-Southern Oscillation, which is well represented by IMDAA. In addition, IMDAA successfully replicated the diurnal cycle of precipitation over the central
<p>The Himalayas, known as the world's third pole, are extremely vulnerable to the ramifications of extreme precipitation events (EPEs), such as flash floods, landslides, and agricultural and infrastructural damages during the Indian summer monsoon (ISM). Complex terrain, high meteorological diversity and uncertainty in observations over this region, make it challenging to comprehend the precipitation disparities and predict the EPEs across the Western Himalayas (WH). Therefore, a better representation of ISM precipitation characteristics over the WH using high-resolution data is crucial for precisely understanding the precipitation variability and mechanisms of climate-triggered localised natural disasters. This study investigates the spatiotemporal variability of precipitation and EPEs using High Asia Refined analysis version 2 (HAR v2), during ISM. It is generated by dynamically downscaling global ERA5 reanalysis data, using Weather Research and Forecasting model (WRF). Before investigating the EPEs, we evaluated HAR v2's ability to represent general characteristics of ISM over the WH against reanalysis, satellite and observational datasets. Preliminary results indicate that, HAR v2 reanalysis better represented the spatiotemporal patterns of precipitation and EPEs across WH. The present study will also investigate the dynamic and thermodynamic processes, associated with EPEs over the study region. Overall, this study aims to provide scientific insights to investigate the potential impacts of climate change on extreme events, which in turn could help mitigate disasters in the Himalayan region. Detailed results of precipitation variability over the Himalayas, and mechanisms altering the atmospheric conditions attributed to the EREs will be discussed.</p><p><strong>Keywords</strong>: Indian Summer Monsoon, Himalayas, HAR-V2 reanalysis, Extreme Precipitation Events</p>
Western Himalayas (WH) have experienced a two-fold temperature increase compared to the Indian sub-continent post-2000, strongly linked to global warming with significant implications for precipitation patterns. Using ERA5 reanalysis, we examine seasonal precipitation changes in the WH between recent (2001–2020) and past decades (1961–2000). Mean summer precipitation has increased over foothills but declined at higher elevations, while winter precipitation has increased region-wide except in certain parts of Jammu-Kashmir (JK), Uttarakhand (UK), and Punjab. In summer, light precipitation has increased in JK, while moderate precipitation has decreased over foothills but enhanced at higher altitudes. Moreover, extreme precipitation has significantly increased in the UK and Himachal Pradesh. During winter, light and extreme precipitation has increased, while moderate and heavy precipitation declined. Maximum one and five-day precipitation extremes (Rx1day, Rx5day) have increased in the foothills with more consecutive wet days. Winter extremes have increased in the northern region, while consecutive dry and wet days have declined, except for specific areas in eastern Ladakh and JK. Furthermore, rising sea surface temperatures, enhanced moisture transport, increased precipitable water and cloud cover in WH are associated with increasing mean and extreme precipitation, emphasizing the impacts of global warming on temperature and precipitation transitions in the region.
<p>The South Asian monsoon is a lifeline of over two billion inhabitants of the Indian subcontinent. Hence, a reliable monsoon prediction system is essential for the operation of weather and climate over the region. The state-of-art General Circulation Models (GCMs) are powerful tools for monsoon prediction and assessing the effects of climate change on precipitation and temperature in rising extreme events such as floods, storms, heatwaves, and drought. However, selecting appropriate GCMs is a grand challenge for assessing climate change projections due to their significant uncertainties. The present study will evaluate the relative performance of GCMs of phases 5 and 6 of the Coupled Model Intercomparison Project dataset based on their multi-model mean (MMM) ability to project rainfall and temperature during the summer season (JJAS) over central India. In addition to the spatial patterns under the Shared Socioeconomic Pathways (SSPs), the study will also examine the model's ability to simulate interannual variability. The present research aims to determine the most reliable CMIP6 and CMIP5 datasets model and their comparison in simulation and projection of seasonal temperature and precipitation. The seasonal climatological mean of GCMs simulated rainfall and temperature shows variability at different scales over central India. CMIP6 multi-model mean demonstrate a reasonably well performance than CMIP5 in the seasonal mean cycle simulation with a better representation of the rainfall. The present study will also investigate the changes in sources of projection uncertainty and future precipitation indices. Finally, the current research will discuss the highlights of comparing the CMIP6 and CMIP5 datasets and<strong>&#160;</strong>their representations of better simulation performances based on the skill score metrics of precipitation and temperature indices.</p><p><strong>KEYWORDS</strong>. CMIP6, CMIP5, MMM precipitation and temperature, Projection</p>
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