JDS Journal of Data Science 1683-8602 1680-743X 1680-743X School of Statistics, Renmin University of China JDS997 10.6339/21-JDS997 Statistical Data Science A Vine Copula Model for Climate Trend Analysis using Canadian Temperature Data ZhuangHaoxin1 DiaoLiqunl2diao@uwaterloo.ca1∗ YiGrace Y.2 Department of Statistics and Actuarial Science, University of Waterloo, ON, Canada Department of Statistical and Actuarial Science, Department of Computer Science, University of Western Ontario, ON, Canada Corresponding author. Email: l2diao@uwaterloo.ca. 20212812021 1913755 Supplementary Material 72020 122020 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

Climate change is widely recognized as one of the most challenging, urgent and complex problem facing humanity. There are rising interests in understanding and quantifying climate changing. We analyze the climate trend in Canada using Canadian monthly surface air temperature, which is longitudinal data in nature with long time span. Analysis of such data is challenging due to the complexity of modeling and associated computation burdens. In this paper, we divide this type of longitudinal data into time blocks, conduct multivariate regression and utilize a vine copula model to account for the dependence among the multivariate error terms. This vine copula model allows separate specification of within-block and between-block dependence structure and has great flexibility of modeling complex association structures. To release the computational burden and concentrate on the structure of interest, we construct composite likelihood functions, which leave the connecting structure between time blocks unspecified. We discuss different estimation procedures and issues regarding model selection and prediction. We explore the prediction performance of our vine copula model by extensive simulation studies. An analysis of the Canada climate dataset is provided.

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