A Data Set for Intercomparing the Transient Behavior of Dynamical Model‐Based Subseasonal to Decadal Climate Predictions. (9th September 2021)

• Record Type: Journal Article
• Authors: Saurral, Ramiro I.; Merryfield, William J.; Tolstykh, Mikhail A.; Lee, Woo‐Sung; Doblas‐Reyes, Francisco J.; García‐Serrano, Javier; Massonnet, François; Meehl, Gerald A.; Teng, Haiyan
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-09
• Subjects: Bias -- climate predictions -- drift -- errors -- initialization shocks; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002570
• Languages: English
• ISSNs: 1942-2466

Abstract

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. Plain Language Summary: The prediction of climate from weeks to years is usually made using complex numerical models that need to have an appropriate representation of the different components of the climate system (atmosphere, oceans, land areas, etc., ) as well as of their interactions. Most of these simulations, however, are affected by errors that originate in part from the numerical methods and approximations used to solve the system of equations of the atmospheric and oceanic systems, and in part by an incorrect representation of the interaction between components. Naturally, these errors lead to a significant decrease in the skill of the predictions, and therefore, must be taken into account. This study presents a new data set made from climate model predictions that have been manipulated in order to be useful for the assessment and further understanding of the physical mechanisms behind the aforementioned errors. The expected increase in our knowledge in the field will help us obtain more skillful climate predictions in the future. Key Points: Initial shocks, drifts, and biases affect climate predictions at all time scales As they usually lead to a substantial reduction of the prediction skill, further efforts toward their understanding are needed The newly developed data set presented in this article is expected to serve as a useful tool for their study