Malayalam is the administrative language of the south Indian state of Kerala and also of the Lakshadweep islands of west coast of India. This work explain an efficient monolingual information retrieval system for Malayalam using query expansion technique, which returns Malayalam documents in response to query in Malayalam. The proposed system uses synonym mapping to improve the efficiency of document retrieval. This technique help to overcome vocabulary mismatch issues by expanding user query with additional relevant terms and reweighting the terms in expanded user query. The developed system is domain independent and used in different Natural Language Processing (NLP) tasks in Classical language like Malayalam.

Information retrieval is an important research area in computer science. Information retrieval is concerned with the storage of text documents and their subsequent retrieval in response to user's requests for information. There are several Information Retrieval models developed for efficient document retrieval. In this survey paper we describe major IR models which are used for various document retrieval purposes. A suitable method for an effective Malayalam monolingual information retrieval system is also proposed here. General Terms

<p><span>The Coupled Model Intercomparison Project Phase 5 (CMIP5) models have showed substantial inter-model spread in estimating annual global-mean precipitation change per one-degree greenhouse-gas-induced warming (precipitation sensitivity), ranging from -4.5</span><span>–4.2</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the Representative Concentration Pathway (RCP) 2.6, the lowest emission scenario, to 0.2–4.0</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the RCP 8.5, the highest emission scenario. The observed-based estimations in the global-mean land precipitation sensitivity during last few decades even show much larger spread due to the considerable natural interdecadal variability, role of anthropogenic aerosol forcing, and uncertainties in observation. This study tackles to better quantify and constrain global land precipitation change in response to global warming by analyzing the new range of Shared Socio-economic Pathway (SSP) scenarios in the </span><span>Coupled Model Intercomparison Project Phase 6 (CMIP6) compared with RCP scenarios in the CMIP5. We show that the range of projected change in annual global-mean land (ocean) precipitation by the end of the 21<sup>st</sup>century relative to the recent past (1995-2014) in the 23 CMIP6 models is over 50% (20%) larger than that in corresponding scenarios of the 40 CMIP5 models. The estimated ranges of precipitation sensitivity in four Tier-1 SSPs are also larger than those in corresponding CMIP5 RCPs. The large increase in projected precipitation change in the highest quartile over ocean is mainly due to the increased number of high equilibrium climate sensitivity (ECS) models in CMIP6 compared to CMIP5, but not over land due to different response of thermodynamic moisture convergence and dynamic processes to global warming. We further discuss key challenges in constraining future precipitation change and source of uncertainties in land precipitation change.</span></p>