Downscaling GCM data using a bias-correction method for the Eastern Mediterranean. - Akyuz, Ezgi; Kaynak, Burcak; Unal, Alper (Copernicus GmbH, 2022)
Climate change and air pollution are two phenomena that can no longer be considered separately. Changes in climate also alter the effects of air pollution. For example, emissions of ammonia (NH3) and non-methane organic compounds (NMVOC), which are precursors of ozone (O3) and secondary particles (PM), are drastically sensitive to temperature and humidity changes. Moreover, the impacts of O3 and secondary PMs on the climate were previously investigated. The first step for air quality modeling studies is the modeling of meteorological fields. In this study, important meteorological parameters in terms of air pollution were obtained from global climate models for a historical and future periods (SSP585). Selected parameters will be corrected and downscaled to a high resolution for Eastern Mediterranean. Then, the bias-corrected and downscaled meteorology outputs will be used in other studies related to air quality. Countries in the Mediterranean Region are being affected significantly by the changing climate due to their location. Previously conducted studies evaluated the meteorological parameters of global climate models with low resolutions. Within the scope of this study, future estimates will be downscaled to a selected domain in Eastern Mediterranean with a spatial resolution of 4×4 km2 However, recent studies have argued that a bias-correction method should be implemented to the selected meteorological parameters prior to downscaling. In previous studies, CMIP simulation outputs were evaluated for Turkey with or without downscaling. There are also studies that biases between observation/reanalysis and GCM model data are calculated. However, according to our knowledge, evaluation of downscaled climate change scenarios in the Mediterranean Region using a bias-correction method has not been conducted yet. Here, a bias correction methodology (Xu et al. (2021)) was used, and an ensemble was generated by choosing appropriate global climate models which are compatible with reanalysis data for the selected region. Native global climate model simulation results and non-linear long-term global climate model simulation trends were evaluated as the preliminary investigation. The temperature means of the global climate models (GCMs) and ERA5 reanalysis data were compared globally and for the EMEP domain. Initial findings showed underestimation or overestimation for the same GCM depending on the selected study domain. This result highlights the importance of the selection of the model for the study domain for weather generation and the models to be chosen for the ensemble. After calculating the long-term non-linear trend, the standard deviations were calculated for the interannual variability for the GCM and ERA5. For the historical period (1979-2014), annual temperature means of BCC-CSM2-MR, CMCC-CM2-SR5, EC-EARTH3, EARTH3-Veg, FIO-ESM-2-0, and KIOST-ESM showed similarity between ERA5 (r2 > 0.70). Summer and fall months show mostly higher correlations compared to other seasons. 22 model ensemble global domain (EMEP Domain) temperature mean, minimum and maximum values were found as 7.62 (6.09), 7.18 (5.28), and 8.12oC (6.89oC), respectively. The values for reanalysis data are 7.95 (6.92), 7.57 (5.94), and 8.27 oC (7.87oC).