Abstract
Precipitation and temperature are very important climatic parameters as their changes may affect life conditions. Therefore, predicting temporal trends of precipitation and temperature is very useful for societal and urban planning. In this research, in order to study the future trends in precipitation and temperature, we have applied scenarios of the fifth assessment report of IPCC. The results suggest that both parameters will be increasing in the studied area (Iran) in future. Since there is interdependence between these two climatic parameters, the independent analysis of the two fields will generate errors in the interpretation of model simulations. Therefore, in this study, copula theory was used for joint modeling of precipitation and temperature under climate change scenarios. By the joint distribution, we can find the structure of interdependence of precipitation and temperature in current and future under climate change conditions, which can assist in the risk assessment of extreme hydrological and meteorological events. Based on the results of goodness of fit test, the Frank copula function was selected for modeling of recorded and constructed data under RCP2.6 scenario and the Gaussian copula function was used for joint modeling of the constructed data under the RCP4.5 and RCP8.5 scenarios.
Highlights
Climate change has become a concern for the scientific community over the past two decades, due to its serious effects on humans, societies, and the environment
In the preparation of the fifth report which was gradually released from 2013 to 2014, the output of CMIP5 series models was used. ese models use new emission scenarios called “RCP” [4]. ese scenarios have four pathways, namely, RCP2.6, RCP4.5, RCP6, and RCP8.5, which are named according to their radiative forcing in 2100 [5]
Pandey et al [27] determined the dependence between temperature parameters (Minimum, Maximum, Mean) and monthly precipitation using 5 copula functions including Gumbel, Frank, T, Clayton, and Normal, of which Normal distribution showed the best fit, so it was used for joint modeling of temperature and precipitation variables
Summary
Climate change has become a concern for the scientific community over the past two decades, due to its serious effects on humans, societies, and the environment. Pandey et al [27] determined the dependence between temperature parameters (Minimum, Maximum, Mean) and monthly precipitation using 5 copula functions including Gumbel, Frank, T, Clayton, and Normal, of which Normal distribution showed the best fit, so it was used for joint modeling of temperature and precipitation variables. Changes of temperature and precipitation as two key climatic parameters influencing natural ecosystems in future in the Kerman Province (southeast Iran) are investigated in the framework of general circulation models under the 3rd and 5th report scenarios. To study interdependence between precipitation and temperature in future under climate change condition, structure of their dependency is determined under copula theory, which lacks the limitations of multivariable distribution functions in using marginal functions. Results of this study can be used for developing strategies in order to reduce the risk of climatic and hydrological phenomena in future
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