IJSRD - International Journal for Scientific Research & Development| Vol. 5, Issue 09, 2017 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1034 Uncertainty Quantification and Reliability Analysis of CMIP5 Projections for the Indian Summer Monsoon Humaid Zahid Siddiqui 1 Mohd Rizwan Ansari 2 Harendra Chauhan 3 Mohd. Abusad Khan 4 Maaz Allah Khan 5 1,2,3,4,5 Azad Institute of Engineering & Technology Engineering, Natkur, India Abstract— A reliable Ensemble Averaging(REA) is a proposed technique which provides an estimate of Associated Uncertainty Range and Reliability of future climate change projections for Indian summer monsoon (June-September), simulated by the state of the art Coupled General Circulation Models (CGCMs) under Coupled Model Inter comparison Project 5 (CMIP5). An evaluation of historical as well as future (RCP4.5 scenario) simulations of ten CGCMs in the REA technique projects a mean monsoon warming of 1.215 0 C with an associated uncertainty range ( ±∆) of 0.22 0 C, and an all-India precipitation increase by 7.109 mm/ month with an associated uncertainty ((±∆P) of 2.592 mm/month for 2021–2050. REA technique also shows a considerable reduction in the uncertainty range compared with the simple average ensemble approach and is characterized by consistently high reliability index in a comparative study with individual CGCMs. The results suggest achievability of REA methodology in constituting the realistic future Indian Monsoon Projections by preparing a performance model and a descriptive confluence criteria. Key words: CMIP5, Reliable Ensemble Averaging (REA) I. INTRODUCTION A Reliability ensemble averaging (REA) technique is proposed to provide a quantitative estimate of associated uncertainty range and reliability of future climate change projections for Indian summer monsoon (June-September), simulated by the state of the art Coupled General Circulation Models (CGCMs) under CMIP5.An evaluation of historical as well as future (RCP4.5 scenario)simulations of ten CGMs in the REA technique projects a mean monsoon warming of 1.215 C, and an all India precipitation increase by 7.109 mm/ month with an associated uncertainty of 2.592 mm/month for 2021-2050. REA technique also reflects a reduction in uncertainty range compared to simpler ensemble average approach and is characterized by consistently high reliability index in a comparative study with individual CGCMs. These results suggest the viability of REA methodology in providing realistic future Indian monsoon projections by incorporating model performance and model convergence criteria. The summer monsoon in India spreads over a tenure of four months (June-September) and it accounts for more than 70 % of the annual rainfall of the country. And is characterized by prominent variability in its onset, pullback, rainfall’s amount and extreme climatic conditions like flood or droughts. All these results have an effect on the water resource, agriculture and economy of the country. There is also an important parameter which has effect on agriculture and water resources which is temperature. Under the scenario of increase in GHGs emission, the monsoon of India is sensitive to global warming. With increasing anthropogenic activities and industrial revolution, there is much concern about how increase in GHGs may affect the Indian monsoon circulation and rainfall. There is only single way to understand the effect of global warming on the monsoon of India and to assess future monsoon climate is to use climate models. This can be achieved based on historical counterfeiting and the new developed RCPs under the CPMIP5. RCP represents the pathways of radiated forcing based on the concept that any one radiated forcing pathway can give consequence from a diverse range of socio- economic and technological scenes. General Circulation Models (GCMs) are one of the basic tools for getting projections of future change in climate. For IPCC 5 th assessment report (AR5), which is set to be released shortly, the couple’s models of CMIP5 have been utilized. To assess the future change in climate, it is important to read the strength and weakness of climate models. A detail study of CMIP3 and CMIP5 models is thus made to understand the ability of climate models in simulating the present day climate. Instead of branding climate models as good or bad, climate scientists use simulations of a range of coupled models to account for the pro and cons of individual GCMs. Since they are mostly qualitative, and such projections are characterized by high level of uncertainty and low level of confidence. Thus, quantification of uncertainty in projecting future scenario of climate for climate change effect assessment and possible mitigation forms a main research focus. Moreover, decision makers in a wide range of organizations, are increasingly searching quantitative climate prognosis, as the impact of change in climate are critical to many stakeholders, including adaptation researchers and resource managers, with an increasing and vulnerable population along with the modification in the usage of land and urbanization, In this article, we lay on a procedure which is quantitative and based on the model performance and model convergence criteria, known as REA We use this method for decisiveness of uncertainty range and the dependence of climate change projections of ten different CMIP5 GCMs for 2 main variables, precipitation and temperature. In the whole article. The term ensemble signifies to simulations of different GCMs and not to different realizations within the same model. Here we analyze projection of climates for all the GCMs under the RCP4.5 scenario. The first criteria in this REA method, whose name is ‘model performance’ is based on the capability of GCMs to replicate the today’s climate. Thus, the better performance of model in this regard, the greater is the reliability of that climate change stimulation. The second criteria, namely ‘model convergence’ is expressed as deviation of individual projection of change with respect to the middle tendency of the ensemble. So a higher weightage is given to the GCMs with lower skill in reproducing the analyzed climate pattern and with lower