Evaluation of CSIRO and LARS WG data accuracy in simulation of climatic variables of East Azerbaijan province

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Rising greenhouse gases and subsequent global warming are among the problems leading to climate change. It directly affects various factors related to human life, so in the present study, the data of the fifth report of CSIRO model under three scenarios RCP8.5, RCP4.5 and RCP2.6 for the next period 2020 2100 as well as two LARS scaling methods WG and Delta method were used to simulate precipitation, minimum and maximum temperatures in East Azerbaijan province. In evaluating the LARS WG model, the error rate of simulation and survey data was evaluated using MSE, RMSE and MAE performance criteria as well as the coefficient of determination and correlation. The results showed that the model is able to predict maximum and minimum temperature parameters with high accuracy, but shows less accuracy in simulating precipitation than other desired variables. Also in Delta Method, the maximum and minimum temperatures are observed for all seasons with an increasing trend, while in the LARS WG model there is a decreasing trend in the next period (2020 2100) for all scenarios. In general, the difference between the operating modes and the LARS WG model for maximum and minimum temperature values in the next period, depending on the type of emission scenario, was obtained between 3.89, 6.33, 7.17 and 2.84. C, respectively. Rainfall has been declining in most seasons under the release scenarios compared to the LARS WG model.

Language:
Persian
Published:
Journal of Climate Change and Climate Disaster, Volume:1 Issue: 2, 2020
Pages:
170 to 198
https://www.magiran.com/p2854413  
سامانه نویسندگان
  • Author (2)
    Farahnaz Khoramabadi
    (1398) دکتری آب و هواشناسی، دانشگاه اصفهان
    Khoramabadi، Farahnaz
اطلاعات نویسنده(گان) توسط ایشان ثبت و تکمیل شده‌است. برای مشاهده مشخصات و فهرست همه مطالب، صفحه رزومه را ببینید.
مقالات دیگری از این نویسنده (گان)