TY - JOUR
T1 - Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre
AU - Polkova, Iuliia
AU - Swingedouw, Didier
AU - Hermanson, Leon
AU - Köhl, Armin
AU - Stammer, Detlef
AU - Smith, Doug
AU - Kröger, Jürgen
AU - Bethke, Ingo
AU - Yang, Xiaosong
AU - Zhang, Liping
AU - Nicolì, Dario
AU - Athanasiadis, Panos J.
AU - Karami, Mehdi Pasha
AU - Pankatz, Klaus
AU - Pohlmann, Holger
AU - Wu, Bo
AU - Bilbao, Roberto
AU - Ortega, Pablo
AU - Yang, Shuting
AU - Sospedra-Alfonso, Reinel
AU - Merryfield, William
AU - Kataoka, Takahito
AU - Tatebe, Hiroaki
AU - Imada, Yukiko
AU - Ishii, Masayoshi
AU - Matear, Richard J.
N1 - Publisher Copyright:
Copyright © 2023 Polkova, Swingedouw, Hermanson, Köhl, Stammer, Smith, Kröger, Bethke, Yang, Zhang, Nicolì, Athanasiadis, Karami, Pankatz, Pohlmann, Wu, Bilbao, Ortega, Yang, Sospedra-Alfonso, Merryfield, Kataoka, Tatebe, Imada, Ishii and Matear.
PY - 2023
Y1 - 2023
N2 - Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.
AB - Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.
KW - Atlantic meridional overturning circulation
KW - data assimilation
KW - decadal predictions
KW - initial conditions
KW - initialization shock
KW - internal variability
KW - prediction skill
KW - subpolar gyre
UR - https://www.scopus.com/pages/publications/85180173227
U2 - 10.3389/fclim.2023.1273770
DO - 10.3389/fclim.2023.1273770
M3 - Article
AN - SCOPUS:85180173227
SN - 2624-9553
VL - 5
JO - Frontiers in Climate
JF - Frontiers in Climate
M1 - 1273770
ER -