Journal of Climatology & Weather Forecasting OPEN ACCESS Freely available online Research Article 1 J o u r n a l o f C l i m a t o l o g y & W e a t h e r F o r e c a s t i n g ISSN: 2332-2594 INTRODUCTION Mountain topography has resulted in distributional variation of local climate (e.g., temperature, air pressure, wind, humidity, fog, hailstorms, and precipitation) in season and space which can be observed even between narrow geographical spaces [1,2]. However, instruments and facilities to record weather patterns or climate changes in the mountain are stationed mostly in valleys or on ridges. Their data cannot be considered always well representative of diverse agro-ecological regions and transitional belts in the surrounding [2-4]. The instrumental data are also not available in many localities, particularly in developing societies. The data from the available facilities have been also affected by many sources of problems. The information based on the data cannot be robust to make policy decisions for socially and environmentally vulnerable societies. The problems include instrumental faults, recording errors, inadequate observations, and missing records. Other issues include standardization flaws (e.g., averaging daily data into weekly, monthly, or yearly figure) and local influences (e.g. dynamic of land- use change). The homogenization approach also has limitations in correcting localized historical problems in the data with few records [5,6]. The errors associated with the problems can make big differences in the results because the climate changes have been mostly determined by fraction changes in the measurement unit. Conflicting findings of climate change studies are potentially associated with data problems [3,7]. Also, the records of most stations are limited to rainfall and temperature dynamics, but other climatic factors (e.g. fog, hailstorm, and humidity) are also important for policy decisions and particularly on adaptation measures. Moreover, human belief and policy decisions on climate change are determined more by human perceptions and experiences. In such a situation, decision-makers require information about the human dimension to devise adaptation measures appropriate for local communities. Information from the instrumental data may not be enough to explain the human stresses experienced from emerging climate change. Experimental studies show that some climatic factors interact with biotic and abiotic factors and result in people’s feeling noticeably different from instrumental records. For example, hotness feeling (measured in human-perceived equivalent temperature) of people depends on the combination levels of temperature, humidity, and airflow factors [8-11]. The interpretation of the human feeling of the hotness feeling can be misleading. Information from alternative sources may help decision- makers in such complicated conditions [12]. Essentially, multiple sources of information would give deeper insights regarding the mechanisms of climate change and increase decision making An Application of the Random Experience Theoretical Model for Investigating Local Climatic Changes in the Himalayan Mountain Region Bhubaneswor Dhakal 1* , Salil Bhattarai 2 , Nischal Dhakal 3 1 University of Otago, Christchurch, New Zealand; 2 Addra-Nepal, Kathmandu, Nepal; 3 Laligunrash Boarding School, Lamjung, Nepal ABSTRACT Instruments of meteorological data collection in the mountain are mostly stationed either on hillsides or in valleys. The data cannot well represent the state of climatic changes in transitional agro-ecological belts in the surroundings. Moreover, the human’s stresses experienced from climatic changes are outcomes of interactions of many abiotic and biotic factors, and cannot be well assessed with the instrumental data. The social science-based study is considered a supporting approach to address the instrumental data problem. Previous social science studies consist of many anomalies in research design, data collection, and analysis, intertation. The weaknesses in those studies are primarily caused by a lack of well-developed theoretical models. To address the methodological problem, this study proposed and illustrated “The Random Experience Model”, a pioneer theoretical framework in climate change study. Applicability of the theoretical framework is demonstrated in a pilot study. A few points worth considering in future studies for demonstrating the strengths of the model are listed. Keywords: Exposure; Extreme-weathers; Mountain; Nepal; Theoretical model; Random-experience Correspondence to: Bhubaneswor Dhakal, University of Otago, Christchurch, New Zealand, E-mail: Bhubaneswordhakal@gmail.com Received: March 16, 2020; Accepted: May 04, 2020; Published: May 15, 2020 Citation: Dhakal B, Bhattarai S, Dhakal N (2020) An Application of the Random Experience Theoretical Model for Investigating Local Climatic Changes in the Himalayan Mountain Region. J Climatol Weather Forecast 8:252. doi: 10.35248/2332-2594.2020.8.252 J Climatol Weather Forecasting, Vol. 8 Iss.2 No: 252 Copyright: ©2020 Dhakal B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.