Investigating the performance of the deterministic and probabilistic versions (multi-member ensemble) of the ERA5 dataset in estimating Iran's temperature

Document Type : Original Article

Authors

Department of Geography, Faculty of Literature and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction: Temperature is a key atmospheric variable and has a direct effect on other atmospheric variables including precipitation. The average global temperature has increased in recent decades, which causes an increase in evapotranspiration, an increase in climate extremes, drought, and wildfires. Examining the performance of different versions of ERA5 can help in choosing the best version of ERA5 in climate studies of the country. This research examines Iran's temperature based on deterministic and probabilistic versions of the ECMWF-ERA5 reanalysis, which replaces the previous releases.
Materials and methods: The data used in this study is the average monthly air temperature which we examined in two ERA5 deterministic and probabilistic datasets. To investigate the impact of horizontal resolution on temperature estimation, two horizontal resolutions of 0.25o and 0.5o from the deterministic and probabilistic versions of ERA5 have been used. Also, the multi-member Ensemble of Data Assimilations (EDA) version with a horizontal resolution of 0.5o has been used to compare ERA5 probabilistic dataset versus its deterministic version. Before evaluating the data of three different versions of ERA5, a multi-member ensemble dataset was generated from 10 different members of the probabilistic version of ERA5, which are presented as Member0 to Member9.
Results and discussion: The results showed that ERA5 correctly estimates the spatial distribution of average temperature in Iran. However, in Iran's higher latitudes and regions with complex topography, the performance of ERA5 in temperature estimation is worse than in arid and semi-arid interior regions. In addition to the complex topography, the ERA5 performance is affected by certain complex geographical features such as the southern coast of the Caspian Sea. The worse performance of ERA5 temperature in the coastal areas of the north of the country and Zagros may reflect some shortcomings in the accurate representation of the geographical features of these areas, such as the air-sea interaction and the simultaneous interaction of local moist currents with the coastline and the complex topography of Alborz and Zagros.
Conclusion: Comparing different versions of ERA5 with observational data showed that the deterministic version of ERA5 with a resolution of 0.5o systematically underestimates the temperature in Iran, which is -7.41% for the area-averaged of the country. On the other hand, the deterministic version with a resolution of 0.5o overestimates the temperature of Iran by 12.01% in the area-averaged of the countryExamining three different versions of the ERA5 dataset for two deterministic and probabilistic versions showed that the deterministic version of ERA5 with a horizontal resolution of 0.25o with an area-averaged bias of 1.07 oC and a percentage of bias of 9.36% shows the best estimate of Iran's temperature. The monthly distribution of Iran's average temperature based on the ERA5 deterministic version with a resolution of 0.25o showed that the minimum temperature of Iran is -10.49 oC in January and the maximum temperature is 39.83 oC in July. The temperature in all months and the annual average follow the topography in Iran. The minimum temperature is negative in 5 months of the year (January, February, March, November, and December) in Iran.

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References

 
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