Geography, asked by DronnChoudhary09, 5 months ago

Evaluate the spatial and seasonal alteration in precipitation in India.

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Answered by Anonymous
19

Answer:

Global warming and climate change is one of the most extensively researched and discussed topical issues affecting the environment. Although there are enough historical evidence to support the theory that climate change is a natural phenomenon, many research scientists are widely in agreement that the increase in temperature in the 20th century is anthropologically related. The associated effects are the variability of rainfall and cyclonic patterns that are being observed globally. In Southeast Asia the link between global warming and the seasonal atmospheric flow during the monsoon seasons shows varying degree of fuzziness. This study investigates the impact of climate change on the seasonality of monsoon Asia and its effect on the variability of monsoon rainfall in Southeast Asia. The comparison of decadal variation of precipitation and temperature anomalies before the 1970s found general increases which were mostly varying. But beyond the 1970s, global precipitation anomalous showed increases that almost corresponded with increases in global temperature anomalies for the same period. There are frequent changes and a shift westward of the Indian summer monsoon. Although precipitation is observed to be 70% below normal levels, in some areas the topography affects the intensity of rainfall. These shifting phenomenon of other monsoon season in the region are impacting on the variability of rainfall and the onset of monsoons in Southeast Asia and is predicted to delay for 15 days the onset of the monsoon in the future. The variability of monsoon rainfall in the SEA region is observed to be decadal and the frequency and intensity of intermittent flooding of some areas during the monsoon season have serious consequences on the human, financial, infrastructure and food security of the region.

Answered by KamaldevSharma
6

Answer:

Hey friend,

Here is the answer you are searching for,

Regional climate models are important tools to examine the spatial and temporal characteristics of rainfall and temperature at high resolutions. Such information has potential applications in sectors like agriculture and health. In this study, the Regional Climate Model Version 3 (RegCM3) has been integrated in the ensemble mode at 55 km resolution over India for the summer monsoon season during the years 1982–2009. Emphasis has been given on the validation of the model simulation at the regional level. In Central India, both rainfall and temperature show the best correlations with respective observed values. The model gives rise to large wet biases over Northwest and Peninsular India. RegCM3 slightly underestimates the summer monsoon precipita-tion over the Central and Northeast India. Nevertheless, over these regions, RegCM3 simulated rainfall is closer to the observations when compared to the other regions where rainfall is overesti-mated. The position of the monsoon trough simulated by the model lies to the north of its original observed position. This is similar to the usual monsoon break conditions leading to less rainfall over Central India. RegCM3 simulated surface maximum temperature shows a large negative bias over the country while the surface minimum temperature is close to the observation. Nevertheless, there is a strong correlation between the all India weighted average surface temperature simulated by RegCM3 and IMD observed values. While examining the extreme weather conditions in Central India, it is found that RegCM3 simulated frequencies of occurrence of very wet days, extremely wet days, warm days and warm nights more often as compared to those in IMD observed values. How-ever, these are systematic biases. The model biases in the frequencies of distribution of rainfall extremes explain the wet and dry biases in different regions in the country. Overall, the inter-annual characteristics of both the rainfall and temperature extremes simulated by RegCM3 in Central India are well in phase with those found in the observed data.

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