Yazar "Irvem, A." seçeneğine göre listele

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    Öğe
    Assessment of seven different global climate models for historical temperature and precipitation in Hatay, Türkiye
    (Springer Nature, 2024) Ozbuldu, M.; Irvem, A.
    Global climate models are important tools for estimating the possible future impacts of climate change and developing necessary adaptation strategies. This study assessed the suitability of global climate models for local climate projections in Hatay, Türkiye. Temperature and precipitation data from different Coupled Model Intercomparison Project Phase 6 climate models were compared with ground-based observations. For stations lacking historical data, multilayer perceptron artificial neural networks were used to generate data. These networks were trained with data from neighboring stations from 1980 to 2014. The most suitable global climate model was determined using a multi-criteria decision-making approach. As a result of the study, it was determined that the multilayer perceptron models effectively generated long-term temperature data with a normalized root mean square error of less than 0.50. Precipitation estimates, while less accurate, achieved reasonable accuracy with a normalized root mean square error of less than 0.70. The evaluation of global climate models revealed a tendency to underestimate minimum temperatures and overestimate maximum temperatures and precipitation. Specifically, the EC-EARTH3, CMCC-ESM2, and MPI-ESM1-2-HR models excelled in maximum temperature estimations; the CMCC-ESM2, GFDL-CM4, and TAIESM1 models were superior for minimum temperatures; and the EC-EARTH3, GFDL-CM4, and MPI-ESM1-2-HR models performed best for precipitation. The findings of this study will provide a framework for the assessment and selection of appropriate climate models for local regions and will help to develop targeted adaptation strategies. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2024.
  • Küçük Resim
    Öğe
    Evaluating the effect of the statistical downscaling method on monthly precipitation estimates of global climate models
    (Global Network Environmental Science & Technology, 2021) Ozbuldu, M.; Irvem, A.
    Researches to foresee the possible effects of climate change on the environment and living beings for taking necessary precautions on time have increased in recent years. In the improvement of these studies, especially the reduction of estimation errors by downscaling the outputs of global climate models played an important role. In this study, the effect of the statistical downscaling method on improving the prediction accuracy of global climate models (GCM) was investigated. For this purpose, a statistical downscaling method based on multiple linear regression was applied to improve monthly precipitation estimates of 3 different GCM (CanESM2, GISS-E2H, and CSIRO Mk 3-6-0) used in future climate predictions. The effect of this method on improving GCM prediction accuracy was determined by comparing the results obtained as a result of scale reduction with the results obtained from the observation station. The predictive parameters for global climate models were determined using downscaling methods by applying correlation analysis for the study area. As a result of this analysis, it was seen that the air temperature and specific humidity values at the pressure level of 925 hPa and the geopotential height value at the 300 hPa pressure level had the best correlation for the years 1970-2005. The usability of three different global climate models for the forecast of future precipitation in the Antakya district of Hatay province was investigated using multiple linear regression analysis, one of the downscaling methods. As a result of the statistical analysis, it was seen that the use of the downscaling method increased the accuracy of all prediction models.