Scholarlink

<p>Global climate models (GCMs) are used to assess historical, current, and future climate change worldwide, including Pakistan. The NASA Earth Exchange Downscaled Projections Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) provides detailed spatiotemporal climate data. This study evaluates NEX-GDDP-CMIP6 models in simulating historical (1951–2014) and future (2015–2100) temperatures projections of minimum (T<sub>min</sub>), maximum (T<sub>max</sub>), and mean (T<sub>mean</sub>) for Pakistan under four Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Historical temperatures simulation was assessed using Taylor diagram and interannual variability skill (IVS) which were chosen for their ability to comprehensively capture both spatial correlation and temporal variability. Additionally, a Comprehensive Ranking Index (CRI) was employed to rank model performance in temperature distribution. The MPI-ESM1-2-HR model ranked highest for temperature distribution accuracy, followed by CanESM5, CNRM-ESM2-1, IPSL-CM6A-LR, GISS-E2-1-G, UKESM1-0-LL, TaiESM1, MIRCO-ES2l, MRI-ESM2-0, and EC-Earth3. The top models were ensembled for future projections, indicating substantial warming under all SSPs, with the highest increase under SSP5-8.5 (6.0–8.0&#xa0;°C), followed by SSP3-7.0 (5.0–6.0&#xa0;°C), SSP2-4.5 (4.0–5.0&#xa0;°C), and SSP1-2.6 (2.0–4.0&#xa0;°C) by the end of the century. Northern and western mountainous areas are projected to warm more significantly (1.0–2.0&#xa0;°C near-term, 2.0–4.0&#xa0;°C mid-term, 3.0–7.0&#xa0;°C long-term) than low-lying plains. These findings guide GCM selection in data-scarce regions and highlight the need for mitigation, adaptation, and resource management strategies in response to projected temperature increases, which could impact agriculture, water resources, and disaster risk.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluation and Projection of Temperatures Over Pakistan: Insights from the Downscaled NEX-GDDP-CMIP6 Models

  • 期刊论文

  • 发表日期：

    2025年1月15日

查看全文
  • Muhammad Aslam Baig,
  • Peng Cui,
  • Safi Ullah,
  • Nazir Ahmed Bazai,
  • Ruilong Wei,
  • Muzaffar Ali,
  • Yan Wang,
  • Waheed Ullah,
  • Iqtidar Hussain,
  • Muhammad Ramzan,
  • Muhammad Asfandyar,
  • Sanju Purohit

摘要

Global climate models (GCMs) are used to assess historical, current, and future climate change worldwide, including Pakistan. The NASA Earth Exchange Downscaled Projections Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) provides detailed spatiotemporal climate data. This study evaluates NEX-GDDP-CMIP6 models in simulating historical (1951–2014) and future (2015–2100) temperatures projections of minimum (Tmin), maximum (Tmax), and mean (Tmean) for Pakistan under four Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Historical temperatures simulation was assessed using Taylor diagram and interannual variability skill (IVS) which were chosen for their ability to comprehensively capture both spatial correlation and temporal variability. Additionally, a Comprehensive Ranking Index (CRI) was employed to rank model performance in temperature distribution. The MPI-ESM1-2-HR model ranked highest for temperature distribution accuracy, followed by CanESM5, CNRM-ESM2-1, IPSL-CM6A-LR, GISS-E2-1-G, UKESM1-0-LL, TaiESM1, MIRCO-ES2l, MRI-ESM2-0, and EC-Earth3. The top models were ensembled for future projections, indicating substantial warming under all SSPs, with the highest increase under SSP5-8.5 (6.0–8.0 °C), followed by SSP3-7.0 (5.0–6.0 °C), SSP2-4.5 (4.0–5.0 °C), and SSP1-2.6 (2.0–4.0 °C) by the end of the century. Northern and western mountainous areas are projected to warm more significantly (1.0–2.0 °C near-term, 2.0–4.0 °C mid-term, 3.0–7.0 °C long-term) than low-lying plains. These findings guide GCM selection in data-scarce regions and highlight the need for mitigation, adaptation, and resource management strategies in response to projected temperature increases, which could impact agriculture, water resources, and disaster risk.