Rainfall in the month of July in India is decided by large-scale monsoon pattern in seasonal to interannual timescales as well as intraseasonal oscillations. India receives maximum rainfall during July and August. Global dynamic models (either atmosphere only or coupled models) have varying skills in predicting the monthly rainfall over India during July. Multi-model ensemble (MME) methods have been utilized to evaluate the skills of five global model predictions for . The objective has been to develop a prediction system to be used in real time to derive the mean of the forecast distribution of monthly rainfall. It has been found that the weighted multi-model ensemble (MME) schemes have higher skill in predicting July rainfall compared to individual models. Through the MME methods, skill of rainfall predictions improved significantly over eastern parts of India. However, there is a region over India where none of the models or the MME scheme has any useful skill. Similarly, there are few typical years in which the mean distribution of July rainfall cannot be predicted with higher skill using the available statistical post-processing methods. A simple MME probabilistic scheme has been utilized to show that skill of probabilistic predictions improved when the representation of mean of forecast distribution has better skill.
P. R. Tiwari, S. C. Kar, U. C. Mohanty, S. Kumari, P. sinha, A. Nair, and S. Dey, 'Skill of precipitation prediction with GCMs over north India during winter season', International Journal of Climatology, Vol. 34 (12): 3440-3455, October 2014, doi: 10.1002/joc.3921. ?? 2017 Royal Meteorological Society, published by Wiley Online Library.This study aims to analyse the skill of state-of-the-art of five general circulation models (GCMs) in predicting winter precipitation over northern India. The precipitation in winter season (December, January and February) is very important for Rabi crops in north India, particularly for wheat, as it supplements moisture and maintains low temperature for the development of the crops. The GCM outputs (seasonal mean forecasts issued in November) from various organizations are compared with the observed high-resolution gridded rainfall data obtained from India Meteorological Department (IMD). Prediction skill of such GCMs is examined for the period 1982???2009. The climatology, interannual standard deviation (ISD) and correlation coefficients have been computed for the five GCMs and compared with observation. It is found that the models are able to reproduce the climatology and ISD to varying degrees; however, skill of predictions is too low. Multi-model ensemble (MME) approaches have been employed. It is found that the weighted MME using multiple linear regression technique improves the prediction skill of winter precipitation over northern India. The teleconnection between the sea surface temperature (SST) and winter precipitation revealed that the SST over the Pacific Ocean affects the precipitation over north India in winter season. While this observed feature is represented well by some models with high fidelity, most models are unable to respond to SST variations in the Pacific Ocean in a realistic manner. Lagged correlations between the north India rainfall and SST over the Nino-3.4 region reveal ?? that only two of the five GCMs get the observed simultaneous teleconnection correctly. Furthermore, only one of these two models has the observed phase lag with the strongest correlation as observed
ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Accepted ManuscriptProjections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries K.C. Pattnayak, S.C. Kar, Mamta Dalal, R.K. Pattnayak PII:S0921-8181 (16) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T Projections of Annual Rainfall and Surface Temperature from CMIP5 Models over the BIMSTEC Countries ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T 2 AbstractBay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) comprising Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand brings together 21% of the world population. Thus the impact of climate change in this region is a major concern for all. To study the climate change, fifth phase of Climate Model Inter-comparison Project models have been used to project the climate for the 21 st century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 over the BIMSTEC countries for the period 1901 to 2100 (initial 105 years are historical period and the later 95 years are projected period). Climate change in the projected period has been examined with respect to the historical period. In order to validate the models, the mean annual rainfall has been compared with observations from multiple sources and temperature has been compared with the data from Climatic Research Unit (CRU) during the historical period. Comparison reveals that ensemble mean of the models is able to represent the observed spatial distribution of rainfall and temperature over the BIMSTEC countries. Therefore, data from these models may be used to study the future changes in the 21 st century. Four out of six models show that the rainfall over India, Thailand and Myanmar has decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka s...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.