Projection of precipitation intensity in Iran using NEX-GDDP by multi-model ensemble approach

Document Type : Research Article

Authors

1 Associate Professor of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran

2 Postdoctoral Researcher of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran

Abstract

Global warming has a significant impact on weather and climate change. These changes, and especially changes in climate extremes, have a great impact on human society and ecosystems. Future changes in extreme climate events, including precipitation extreme, will cause great damage to society, the economy, and ecosystems because of their potentially severe effects. The purpose of this study is to investigate the performance of NASA earth exchange global daily downscaled projections (NEX-GDDP) in simulating precipitation and its long-term projection in Iran.
    For this purpose, the nine models of NEX-GDDP were selected based on climate sensitivity. Precipitations from 49 ground stations during the historical period (1980-2005) were used to evaluate the precipitation output of the mentioned models using RMSE and MBE statistics. The Bayesian model averaging (BMA) method was used to generate an ensemble model from nine models. Intensity of precipitation with two indices SDII and RX1day is projected during the three periods of near future (2026-2050), medium future (2051-2075) and far future (2076-2100) under two scenarios RCP4.5 and RCP8.5.
    The results showed that NEX-GDDP models did not have much bias compared to observation and most models with low relative error have good performance in reproducing the spatial pattern of precipitation in Iran. Among the nine selected models, MPI-ESM-LR model has shown the maximum overestimation and IPSL-CM5A-LR model has shown the maximum underestimation in Iran. Compared to other GCMs in the historical period, NEX-GDDP models show less uncertainty at the regional scale;therefore, NEX-GDDP simulations are much more reliable. The precipitation intensity projections show that in the future, precipitation will occur more intensively throughout Iran. The RX1day and SDII indices will increase by about 4 to 13 percent for the average area of Iran by the end of the century, which indicates an increase in flooding in Iran in the coming decades.
    Projections of precipitation intensity in Iran based on two indices RX1day and SDII from the set of precipitation index of ETCCDI working group by ensemble model NEX-GDDP-MME showed that with the continuation of global warming, precipitation intensity will increase significantly throughout Iran. The maximum one-day precipitation amount (RX1day) will increase between 4.42 to 13.08 percent for the area-averaged by the end of this century compared to 1980-2005. Moreover, the SDII index will increase between 4.45 to 13.96% for area-average of Iran. The highest increase in precipitation intensity generally occurs in the coastal region of southern Iran, especially in the coasts of the Persian Gulf and western Iran, while the lowest increase is generally observed in the northwestern region.
 





Introduction

Global warming has a significant impact on weather and climate change. These changes, and especially changes in climate extremes, have a great impact on human society and ecosystems. Future changes in extreme climate events, including precipitation extreme, will cause great damage to society, the economy, and ecosystems because of their potentially severe effects. The purpose of this study is to investigate the performance of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) in simulating precipitation and its long-term projection in Iran.

Materials and methods

For this purpose, the nine models of NEX-GDDP were selected based on climate sensitivity. Precipitation from 49 ground stations during the historical period (1980-2005) were used to evaluate the precipitation output of the mentioned models using RMSE and MBE statistics. The Bayesian model averaging (BMA) method was used to generate an ensemble model from nine models. Intensity of precipitation with two indices SDII and RX1day is projected during the three periods of near future (2026-2050), medium future (2051-2075) and far future (2076-2100) under two scenarios RCP4.5 and RCP8.5.

Results and discussion

The results showed that NEX-GDDP models did not have much bias compared to observation and most models with low relative error have good performance in reproducing the spatial pattern of precipitation in Iran. Among the nine selected models, MPI-ESM-LR model has shown the maximum overestimation and IPSL-CM5A-LR model has shown the maximum underestimation in Iran. Compared to other GCMs in the historical period, NEX-GDDP models show less uncertainty at the regional scale, and therefore NEX-GDDP simulation are much more reliable. The precipitation intensity projections show that in the future, precipitation will occur more intensively throughout Iran. The RX1day and SDII indices will increase by about 4 to 13 percent for the average area of Iran by the end of the century, which indicates an increase in flooding in Iran in the coming decades.

Conclusion

Projections of precipitation intensity in Iran based on two indices RX1day and SDII from the set of precipitation index of ETCCDI working group by ensemble model NEX-GDDP-MME showed that with the continuation of global warming, precipitation intensity will increase significantly throughout Iran. The maximum one-day precipitation amount (RX1day) will increase between 4.42 to 13.08 percent for the area-averaged by the end of this century compared to 1980-2005. Also, the SDII index will increase between 4.45 to 13.96% for area-average of Iran. The highest increase in precipitation intensity generally occurs in the coastal region of southern Iran, especially in the coasts of the Persian Gulf and western Iran, while the lowest increase is generally observed in the northwestern region.

Keywords

Keywords

Main Subjects

References

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Iranian Journal of Geophysics
Volume 16, Issue 1
May 2022
Pages 47-68

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