Temperature and Precipitation Bias Correction in the Climate Model Simulations

Document Type : Original Article

Author

PhD of Climatology, Iran Meteorology Organisation

Abstract

Bias correction usually used for the outputs of the climatic model before use as an input of environmental models in the climate change effects studies. In this research, GCM outputs obtained from ESGF dataset with the RcgCM4-4 climate model in the South Asia CORDEX domain with horizontal resolution about 50 km. Precipitation, maximum and minimum temperature data of 41 synoptic stations with the closest distance to model cells in Iran domain obtained from Iran Meteorological Organization. Then the accuracy of the outputs of the cells corresponding compared to the observational data with correlation and normalized standard deviation methods evaluated by Taylor diagram. Then, the model bias with the least error corrected for the precipitation outputs with fitQmapRQUANT method and for the maximum and minimum temperature outputs with the linear scanning bias correction. Results showed that the bias correction methods used for the temperature outputs improved the error of data but for precipitation outputs, it was not effective due to the large difference between observation and model data. The model to estimating the maximum temperature in these regions had less bias than in those at low latitudes. The CCCma model's monthly minimum temperature outputs overestimated this variable, especially in hot seasons, compared to station data in southern Iran. At most stations located at high latitudes, this variable is corrected or underestimated. The bias correction of the model outputs for this variable corrected the biases in the cells corresponding to the observation stations.

Keywords

References

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