Iran-Water Resources Research

Iran-Water Resources Research

Temperature and Precipitation Projections in the Khazar (Caspian) Sea Basin by Evaluating the Effect of Bias Correction Methods and Using Ensemble of CMIP6 Climate Models

Document Type : Original Article

Authors
Narges Azad 1
Azadeh Ahmadi 2
1 Ph.D. Candidate, Faculty of Civil, Water and Environmental Engineering, Shadid Beheshti University, Tehran, Iran.
2 Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shadid Beheshti University, Tehran, Iran.
10.22034/iwrr.2023.180043
Abstract
General Circulation Models (GCMs) are indispensable tools for understanding future climate change. However, these models introduce several sources of uncertainty into the environmental and hydrological models with regards to climate variables and assessing the effects of climate change. Moreover, the utilization of GCM output data requires an evaluation of its accuracy in comparison to observational data. The aim of this study is to compare the outputs of 13 CMIP6 models with observations using two bias correction methods called Quantile Mapping and Linear Scaling, along with a Bayesian model averaging approach. To explore the future changes in temperature and precipitation variables in the Khazar (Caspian) Sea basin, climate model simulations in two scenarios, SSP245 and SSP585, were chosen. The results showed that the quantile mapping method, despite its better performance in modifying climate models, is inefficient in stations where the bias is unstable over time and the correction is not done properly. Also, using the Multi Models Ensemble (MME) provides a higher level of accuracy than individual models. Although model averaging after bias correction reduces the extreme fluctuations of the time series, it has a better accuracy and performance than the second method (model averaging before correction and then applying bias correction). The findings showed that the average precipitation on Khazar basin for the two scenarios, SSP585 and SSP245, is not statistically different from the observational periods and ranges from 0% to 6% for both the near-future period (2021–2050) and the far-future period (2051–2080). The average temperature under the SSP245 (SSP585) scenario will rise 1.67 and 3.14 (1.8 and 3.65)  more than it did during the observational periods for the near-future and the far-future, respectively.
Keywords
Precipitation
Temperature
Climate Change
Bias Correction
Ensemble
Bayesian Model Averaging

Subjects

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  • Receive Date 26 July 2023
  • Revise Date 21 September 2023
  • Accept Date 24 September 2023