A framework for assessing uncertainties in drought projections under climate change: Insights from CMIP6 models DOI
Omid Zabihi, Azadeh Ahmadi, Ali Torabi Haghighi

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 982, P. 179679 - 179679

Published: May 17, 2025

Language: Английский

Plant endemic diversity in the Irano-Anatolian global biodiversity hotspot is dramatically threatened by future climate change DOI Creative Commons
Halime Moradi, Jalil Noroozi, Yoan Fourcade

et al.

Biological Conservation, Journal Year: 2025, Volume and Issue: 302, P. 110963 - 110963

Published: Jan. 8, 2025

Language: Английский

Citations

3

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran DOI Creative Commons

Narges Azad,

Azadeh Ahmadi

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 15, 2024

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities nonstationarity R criterion compared to others. Although according ranking raw bias-corrected outputs CMIP6 GCMs for Iran, NorESM2-MM, AWI-ESM-1-1-LR, MPI-ESM1-2-LR models consistently among top six ranked precipitation both corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, IITM-ESM GCMs. Bias correction methods, including quantile mapping linear scaling, integrated with Bayesian averaging, were applied. While demonstrates superior performance less disparity than it proves ineffective correcting biases bias over time. RMSE monthly ranges from almost 0 200 mm, a large value related high stations, temperature exhibits range 4 °C. use multi-model ensemble improves individual models, resulting reduction differences between minimum maximum values 178.6 91.0. Additionally, mean absolute error decreases 126.9 93.3, difference correlation coefficient narrows 0.9 0.42. Averaging after prevents significant fluctuations while maintaining higher accuracy, contrast second method, which involves bias-correcting averaging.

Language: Английский

Citations

3

A framework for assessing uncertainties in drought projections under climate change: Insights from CMIP6 models DOI
Omid Zabihi, Azadeh Ahmadi, Ali Torabi Haghighi

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 982, P. 179679 - 179679

Published: May 17, 2025

Language: Английский

Citations

0