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<p>Global warming and climate change pose a serious threat to human life on earth. General circulation models (GCMs) are mathematical representations of the climate system to simulate the Earth’s climate for better future projection. The bias in GCM simulations can significantly impact the reliability of climate projections. The model ensemble system improves the predictive accuracy by considering the performance of each GCM. This study aims to provide a novel approach to weighting an ensemble, based on minimization of mean square distance (MSD) of each bias corrected simulation from the observed data and to reduce the impact of outliers and extreme values in each simulation. The present study implements three bias correction (BC) methods namely: linear scaling (LS), variance scaling (VS) and quantile mapping (QM) to correct the bias in monthly temperature simulations of 20 GCMs from coupled model intercomparison project phase 6 (CMIP6) over Tibetan Plateau region. The proposed methodology provides optimal combination of weights for MME by showing higher correlation of 0.9568 on average, with decreased values of normalized root mean square error (NRMSE) and normalized relative absolute error (NRAE) of 0.1484 and 0.6751, respectively, in estimating monthly temperature. Based on the improved prediction of historical monthly temperature data the proposed method also provides a reliable future projection for three future scenarios: SSP1-2.6, SSP2-4.5 and SSP5-8.5 for the time period 2015–2100. The future projections for three SSPs reveal the annual average rise in temperature per year by 0.02–0.08&#xa0;°C considering all three scenarios. A standardized temperature index (STI) has also been developed to observe the pattern of extreme temperature events in future for these scenarios. The results indicate that the frequency of “extremely hot” events is low across all scenarios i.e., 0.194–0.291% but overall, the temperature seems to rise in future with gradually increasing trend. The frequency of “very hot” events is higher under SSP5-8.5, indicating that higher emissions scenarios are associated with a greater likelihood of very hot conditions. The normal temperature conditions may also vary depending on the intensity of emissions.</p>

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A novel procedure for monitoring temperature characteristics using multiple climate projections

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Rashida Khalil,
  • Zulfiqar Ali

摘要

Global warming and climate change pose a serious threat to human life on earth. General circulation models (GCMs) are mathematical representations of the climate system to simulate the Earth’s climate for better future projection. The bias in GCM simulations can significantly impact the reliability of climate projections. The model ensemble system improves the predictive accuracy by considering the performance of each GCM. This study aims to provide a novel approach to weighting an ensemble, based on minimization of mean square distance (MSD) of each bias corrected simulation from the observed data and to reduce the impact of outliers and extreme values in each simulation. The present study implements three bias correction (BC) methods namely: linear scaling (LS), variance scaling (VS) and quantile mapping (QM) to correct the bias in monthly temperature simulations of 20 GCMs from coupled model intercomparison project phase 6 (CMIP6) over Tibetan Plateau region. The proposed methodology provides optimal combination of weights for MME by showing higher correlation of 0.9568 on average, with decreased values of normalized root mean square error (NRMSE) and normalized relative absolute error (NRAE) of 0.1484 and 0.6751, respectively, in estimating monthly temperature. Based on the improved prediction of historical monthly temperature data the proposed method also provides a reliable future projection for three future scenarios: SSP1-2.6, SSP2-4.5 and SSP5-8.5 for the time period 2015–2100. The future projections for three SSPs reveal the annual average rise in temperature per year by 0.02–0.08 °C considering all three scenarios. A standardized temperature index (STI) has also been developed to observe the pattern of extreme temperature events in future for these scenarios. The results indicate that the frequency of “extremely hot” events is low across all scenarios i.e., 0.194–0.291% but overall, the temperature seems to rise in future with gradually increasing trend. The frequency of “very hot” events is higher under SSP5-8.5, indicating that higher emissions scenarios are associated with a greater likelihood of very hot conditions. The normal temperature conditions may also vary depending on the intensity of emissions.