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Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran

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Abstract

This study evaluates the future climate fluctuations in Iran’s eight major climate regions (G1–G8). Synoptic data for the period 1995–2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020–2040), middle future (2040–2060), and far future (2060–2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson’s correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995–2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by −74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by −4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

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Acknowledgements

The authors would like to reveal their gratitude and appreciation to the Iranian Meteorological Organization and Iran Water Resources Management Company for providing data.

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Authors and Affiliations

  1. Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyed Babak Haji Seyed Asadollah & Ahmad Sharafati

  2. School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Malaysia

    Shamsuddin Shahid

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  1. Seyed Babak Haji Seyed Asadollah
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Contributions

Seyed Babak Haji Seyed Asadollah carried out the review analysis and modeling and participated in drafting the manuscript. Ahmad Sharafati proposed the topic, participated in coordination, and aided in interpreting results and paper editing. Shamsuddin Shahid carried out the investigation and paper editing. All authors read and approved the final manuscript.

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Correspondence to Ahmad Sharafati.

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Asadollah, S.B.H.S., Sharafati, A. & Shahid, S. Application of ensemble machine learning model in downscaling and projecting climate variables over different climate regions in Iran. Environ Sci Pollut Res 29, 17260–17279 (2022). https://doi.org/10.1007/s11356-021-16964-y

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