Near term (2021-2028) climate prediction of monthly temperature in Iran using Decadal Climate Prediction Project (DCPP)

Document Type : Research Article

Authors

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

2 Post-Doc Researcher, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

3 M.Sc. Graduated, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Decadal prediction is a general term that encompasses predictions for annual, interannual, and decadal periods in which significant progress has been made over the years. Decadal climate prediction is made using a hindcast and the latest generation of climate models. It provides two categories of hindcast and prediction data. The purpose of this study is to evaluate the temperature from the DCPP and its prediction in Iran based on the available models of the DCPP project contribution to the CMIP6 project.
The study area of this research is Iran. As mentioned, the purpose of this study is to predict the near-term temperature based on the output of the DCPP project. For this purpose, daily temperature from 42 synoptic stations was used as observation to evaluate the available models of the DCPP project. Unlike general circulation models (GCMs), the DCPP project has an initialization that includes a three-month time step for implementation of each year. Air temperature of two models BCC-CSM2-MR and MPI-ESM1-2-HR with a horizontal resolution of 100 km is available for the DCPP project from the CMIP6 series. Three statistics, Pearson correlation coefficient (PCC), root mean square error (RMSE) and mean bias error (MBE), were used to evaluate the selected models of the DCPP project using observational data (synoptic stations).
In the study of the relationship between observation and hindcast of the two selected models, it is found that the BCC-CSM2-MR model shows a high correlation (0.99) in the mountainous areas of Zagros and Alborz and arid and semi-arid regions of the inland and east of Iran. However, the northern and southern coasts show a weak correlation (between 0.92 and 0.97). Examination of RMSE statistics for the BCC-CSM2-MR model also shows the maximum error between 1.2 to 2.2o in the coastal areas of the country (the Caspian Sea and the Oman Sea). The western and northern mountains of Iran show the minimum RMSE.
The BCC-CSM2-MR model shows more bias than the MPI-ESM1-2-HR model in the northern regions of the country. Examination of the average monthly temperature anomaly across Iran in the predicted period compared to the hindcast period (1980-2019) showed that the monthly temperature anomaly is positive across the country compared to the normal period in all months of the year. This value is 1.03 degrees Celsius for the country-wide average. In other words, the temperature in Iran will increase by one degree for the bear term period (2021-2028) compared to the long-term period of the last 40 years (1980-2019).
In this study, for the first time, a decadal climate prediction of Iran's monthly temperature is assessed using the output of two available models BCC-CSM2-MR and MPI-ESM1-2-HR from the DCPP contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The evaluation of the models using three statistical measures RMSE, MBE and PCC showed that the BCC-CSM2-MR model has the lowest performance in the coastal areas of Iran (the Caspian and the Oman Sea) and the highest performance in the highlands of Iran. The output of the MPI-ESM1-2-HR model during the hindcast period (1980-2019) show good performance of this model in determining the temperature patterns of the country. The minimum temperature is based on the output of this model in January with a value of -6.28o. Examination of the predicted temperature anomaly (2021-2028) compared to the hindcast period (1980-2019) shows that the average anomaly across the country for different months of the year during 2021-2028 compared to the hindcast period is 0.99o.

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References

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Journal of the Earth and Space Physics
Volume 48, Issue 1
online publication date: 9 May 2022
May 2022
Pages 189-211

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