The study has been planned to project the climate change under different SSP scenarios by evaluating the performance of different bias removal methods for the Ludhiana district situated in central region of Punjab using sACCESS-CM2 model. For this purpose, the daily global data for maximum temperature, minimum temperature and rainfall by the end of twenty-first century were downloaded from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) using outputs of ACCESS-CM2 model under four SSP scenarios (SSP 126, SSP 245, SSP 370 and SSP 585) which was further extracted for the for Ludhiana district of Punjab using the GIS software. The bias correction was performed by dividing the observed and model climate data into calibration and validation sets. The bias correction of extracted model data (maximum temperature, minimum temperature and rainfall) was done by developing correction functions (using a model and observed data from (Meinshausen, and Meinshausen, 1970) to 2000) from different bias correction methods. These correction functions were used to correct the model data from 2001–2023 which was validated against the observed data for same period. Thereafter, statistical evaluation of these methods was done to check the performance and efficiency in improving the accuracy of climate projections. The best bias correction method was selected for further correction of future data under different scenarios. The results indicated that performance of the linear scaling method was good than other methods as indicated by less error and more efficiency values. It was followed by quantile mapping and difference method. The projections showed that maximum temperature projected to rise significantly under future SSP scenarios, with SSP585 showing more annual increase of 3.1 °C compared to SSP126 (+ 2 °C). Among the different seasons, the maximum temperature during kharif, rabi, monsoon, winter, pre-monsoon and post monsoon seasons are expected to rise under different scenarios by 2.7 to 4.1 °C, 2.5 to 3.4 °C, 3 to 4.4 °C, 0.9 to 1.9 °C, 1.6 to 3.1 °C and 1.8 to 2 °C, respectively under the different climate change scenarios. Monthly trends indicate notable increases in summer months, particularly in June 4.3 °C and September 4.3 °C and winter months showing 3.6 to 3 °C rise. Minimum temperature shows projected rise with SSP585 showing more annual increase 4.2 °C compared to SSP126 2.7 °C. Among the different seasons, the minimum temperature during kharif, rabi, monsoon, winter, pre-monsoon and post monsoon seasons are expected to rise under different scenarios by 3.3 to 5 °C, 1.9 to 3.3 °C, 3.7 to 5.4 °C, 1.7 to 3.3 °C, 1.9 to 3.5 °C and 2.6 to 3.8 °C respectively under the different climate change scenarios. Monthly trends indicate notable increases in summer months, particularly in June 4.3 °C and September 4.6 °C and winter months showing 3 to 3.2 °C rise. Rainfall is projected to increase with SSP585 showing a higher annual rise of + 99 mm compared to + 58.4 mm under SSP126. Among the different seasons, rainfall during kharif, rabi, monsoon, winter, pre-monsoon and post-monsoon seasons is expected to change under different scenarios by + 72.3 to + 221.8 mm, –15.1 to + 22.1 mm, + 44.1 to + 205.3 mm, –11.8 to + 4.7 mm, + 6.8 to + 38 mm and –13.3 to –4.1 mm respectively under the different climate change scenarios. It has been concluded that climate would be warmer and drier with intense extreme events in the future and rainfall would become more seasonal with intensified monsoon surpluses and reduced post-monsoon and winter rainfall in the future.

CMIP6 model-based projections in temperature and rainfall for Ludhiana district of Punjab, India

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发表日期：

2025年10月22日

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Jaspreet Singh,
Navneet Kaur,
Sukhjeet Kaur,
Prabhjyot-Kaur,
Raj Setia

摘要

The study has been planned to project the climate change under different SSP scenarios by evaluating the performance of different bias removal methods for the Ludhiana district situated in central region of Punjab using sACCESS-CM2 model. For this purpose, the daily global data for maximum temperature, minimum temperature and rainfall by the end of twenty-first century were downloaded from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) using outputs of ACCESS-CM2 model under four SSP scenarios (SSP 126, SSP 245, SSP 370 and SSP 585) which was further extracted for the for Ludhiana district of Punjab using the GIS software. The bias correction was performed by dividing the observed and model climate data into calibration and validation sets. The bias correction of extracted model data (maximum temperature, minimum temperature and rainfall) was done by developing correction functions (using a model and observed data from (Meinshausen, and Meinshausen, 1970) to 2000) from different bias correction methods. These correction functions were used to correct the model data from 2001–2023 which was validated against the observed data for same period. Thereafter, statistical evaluation of these methods was done to check the performance and efficiency in improving the accuracy of climate projections. The best bias correction method was selected for further correction of future data under different scenarios. The results indicated that performance of the linear scaling method was good than other methods as indicated by less error and more efficiency values. It was followed by quantile mapping and difference method. The projections showed that maximum temperature projected to rise significantly under future SSP scenarios, with SSP585 showing more annual increase of 3.1 °C compared to SSP126 (+ 2 °C). Among the different seasons, the maximum temperature during kharif, rabi, monsoon, winter, pre-monsoon and post monsoon seasons are expected to rise under different scenarios by 2.7 to 4.1 °C, 2.5 to 3.4 °C, 3 to 4.4 °C, 0.9 to 1.9 °C, 1.6 to 3.1 °C and 1.8 to 2 °C, respectively under the different climate change scenarios. Monthly trends indicate notable increases in summer months, particularly in June 4.3 °C and September 4.3 °C and winter months showing 3.6 to 3 °C rise. Minimum temperature shows projected rise with SSP585 showing more annual increase 4.2 °C compared to SSP126 2.7 °C. Among the different seasons, the minimum temperature during kharif, rabi, monsoon, winter, pre-monsoon and post monsoon seasons are expected to rise under different scenarios by 3.3 to 5 °C, 1.9 to 3.3 °C, 3.7 to 5.4 °C, 1.7 to 3.3 °C, 1.9 to 3.5 °C and 2.6 to 3.8 °C respectively under the different climate change scenarios. Monthly trends indicate notable increases in summer months, particularly in June 4.3 °C and September 4.6 °C and winter months showing 3 to 3.2 °C rise. Rainfall is projected to increase with SSP585 showing a higher annual rise of + 99 mm compared to + 58.4 mm under SSP126. Among the different seasons, rainfall during kharif, rabi, monsoon, winter, pre-monsoon and post-monsoon seasons is expected to change under different scenarios by + 72.3 to + 221.8 mm, –15.1 to + 22.1 mm, + 44.1 to + 205.3 mm, –11.8 to + 4.7 mm, + 6.8 to + 38 mm and –13.3 to –4.1 mm respectively under the different climate change scenarios. It has been concluded that climate would be warmer and drier with intense extreme events in the future and rainfall would become more seasonal with intensified monsoon surpluses and reduced post-monsoon and winter rainfall in the future.