Open AccessA Data Set for Intercomparing the Transient Behavior of Dynamical Model‐Based Subseasonal to Decadal Climate Predictions
Author(s) -
Saurral Ramiro I.,
Merryfield William J.,
Tolstykh Mikhail A.,
Lee WooSung,
DoblasReyes Francisco J.,
GarcíaSerrano Javier,
Massonnet François,
Meehl Gerald A.,
Teng Haiyan
Publication year - 2021
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002570
Subject(s) - hindcast , climatology , set (abstract data type) , forecast skill , data set , range (aeronautics) , climate model , coupled model intercomparison project , transient (computer programming) , computer science , environmental science , meteorology , climate change , geology , oceanography , materials science , artificial intelligence , composite material , programming language , operating system , physics
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.