Masoudi et al. (2025) Spatial and Statistical Analysis of Climate Change in the Middle East: A Study of Precipitation and Temperature Variability Using NOAA Weather Data and Geostatistical Methods

Identification

• Journal: Earth Systems and Environment
• Year: 2025
• Date: 2025-10-28
• Authors: Masoud Masoudi, Elham Asrari, Ali Reza Younesfard, Ali Torabi Haghighi
• DOI: 10.1007/s41748-025-00802-z

Research Groups

• Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz, Iran
• Department of Civil Engineering, Payame Noor University, Tehran, Iran
• Water, Energy, and Environmental Engineering Research Unit, University of Oulu, Oulu, Finland

Short Summary

This study provides the first comprehensive spatial and statistical analysis of precipitation, temperature, and the De Martonne climate index variability across the entire Middle East from 1979 to 2017. It reveals a significant warming trend (average 1.32 °C) and a complex precipitation pattern with an average decline of 7.58%, leading to increased aridity in the region.

Objective

• To provide a detailed spatial and statistical analysis of changes in precipitation, temperature, and the De Martonne climate index across the Middle East from 1979 to 2017.

Study Configuration

• Spatial Scale: Entire Middle East region, covering approximately 7,200,000 square kilometers, analyzed using 708 grid points at 1° x 1° resolution.
• Temporal Scale: 1979 to 2017 (39 years).

Methodology and Data

• Models used:
  • De Martonne climate index calculation.
  • Nonparametric Mann-Kendall trend test for significance assessment.
  • Geostatistical interpolation techniques: Kriging (Simple Kriging, Ordinary Kriging) and Inverse Distance Weighting (IDW).
  • Linear regression for trend line derivation.
  • Geographic Information System (GIS) environment (ArcGIS 10.6) for spatial analysis and mapping.
• Data sources:
  • Monthly average temperature and monthly precipitation data from the NOAA (National Oceanic and Atmospheric Administration) website (www.esrl.noaa.gov).
  • Data prepared by the University of Delaware (UD).
  • NOAA data collection integrates NOAA satellites (e.g., AVHRR sensor), advanced weather radars, weather buoys, and ground-based weather stations.

Main Results

• Temperature:
  • Approximately 91% of the studied area experienced statistically significant temperature increases.
  • The average temperature increase across the Middle East was 1.32 °C during the study period, exceeding the global average.
  • Hotspots, particularly in the southern Arabian Peninsula, observed temperature rises exceeding 6 °C.
  • Temperature increases ranged from 1 °C to 2 °C across nearly half of the Middle East.
  • Only 9 points showed a significant cooling trend, and 110 points showed no significant trend.
• Precipitation:
  • 62% of the region showed no statistically significant change in precipitation.
  • 31% of the region experienced declines in precipitation.
  • Only 7% of the region reported an increase in precipitation.
  • Overall average rainfall decreased by 7.58% during the study period.
  • Most precipitation decreases were observed in Yemen, central parts of the region, and northern Iran, while increases were noted in central Iran and western Saudi Arabia.
• De Martonne Climate Index (Aridity):
  • 66% of the region exhibited insignificant changes in the De Martonne climate index.
  • 30% of the region showed a significant intensification of climate aridity (drier trends).
  • 4% of the region showed more humid trends.
  • The average decrease in the climate change trend (De Martonne index) was -0.62, indicating a slight shift towards a drier climate.
  • Significant drying trends were observed in northern Iran, northern and western Iraq, eastern Syria, and the northern and southern parts of the Arabian Peninsula, while more humid conditions were found in the Central Zagros area of Iran.
• Methodology Performance:
  • Simple Kriging with an exponential variogram model demonstrated the highest statistical accuracy for interpolating changes in precipitation and temperature.
  • Ordinary Kriging with an exponential variogram model showed the highest statistical accuracy for mapping climate change based on the De Martonne index.
  • Both Kriging methods proved more effective in mapping climate parameter changes compared to the IDW method, as indicated by RMSE values.

Contributions

• First comprehensive spatial and statistical evaluation of recent climate change hazards across the entire Middle East region over the past 40 years (1979–2017).
• Utilizes a high-resolution dataset (708 grid points at 1° x 1° resolution) covering the entire Middle East, addressing limitations of previous studies that often focused on smaller areas or lower resolutions.
• Expands the scope beyond temperature-only analyses by integrating precipitation and the De Martonne aridity index, providing a more holistic understanding of regional climate dynamics.
• Employs robust geostatistical methods (Kriging) within a GIS framework and nonparametric statistical tests (Mann-Kendall) to generate highly accurate spatial trend maps and assess statistical significance.
• Provides actionable insights based on observed historical and recent climate data, offering a more precise viewpoint for policymakers compared to uncertain future climate scenarios, crucial for immediate adaptation and mitigation strategies.
• Highlights the accelerated warming in the Middle East relative to global trends and the complex, uneven distribution of precipitation changes, emphasizing the urgent need for location-specific responses to intensifying aridification and warming.

Funding

The authors declare that no grants, funds, or other financial support were received for this research.

Citation

@article{Masoudi2025Spatial,
  author = {Masoudi, Masoud and Asrari, Elham and Younesfard, Ali Reza and Haghighi, Ali Torabi},
  title = {Spatial and Statistical Analysis of Climate Change in the Middle East: A Study of Precipitation and Temperature Variability Using NOAA Weather Data and Geostatistical Methods},
  journal = {Earth Systems and Environment},
  year = {2025},
  doi = {10.1007/s41748-025-00802-z},
  url = {https://doi.org/10.1007/s41748-025-00802-z}
}