A Data Set for Intercomparing the Transient Behavior of Dynamical Model‐Based Subseasonal to Decadal Climate Predictions

Journal of Advances in Modeling Earth Systems

Francisco J. Doblas‐Reyes¹  William J. Merryfield²  Woo‐Sung Lee²  Javier García‐Serrano³  Mikhail A. Tolstykh⁴  Gerald A. Meehl⁵  Haiyan Teng⁶  Ramiro I. Saurral⁷  François Massonnet⁸ 

[1] Barcelona Supercomputing Center (BSC) Barcelona Spain;Canadian Centre for Climate Modeling and Analysis Environment and Climate Change Canada Victoria British Columbia Canada;Group of Meteorology Universitat de Barcelona (UB) Barcelona Spain;Marchuk Institute of Numerical Mathematics Russian Academy of Sciences Moscow Russia;National Center for Atmospheric Research Climate and Global Dynamics Laboratory Boulder CO USA;Pacific Northwest National Laboratory Richland WA USA;Universidad de Buenos Aires Facultad de Ciencias Exactas y Naturales Departamento de Ciencias de la Atmósfera y los Océanos Buenos Aires Argentina;Université Catholique de Louvain Georges Lemaître Centre for Earth and Climate Research Earth and Life Institute Louvain‐la‐Neuve Belgium;

关键词: Bias;    climate predictions;    drift;    errors;    initialization shocks;   

DOI  :  10.1029/2021MS002570

来源: DOAJ

【 摘 要 】

Abstract Climate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions.

【 授权许可】

Unknown