Dynamical drivers of Greenland blocking in climate models

• Main Authors: Michel, Clio, Madonna, Erica, Spensberger, Clemens, Li, Camille, Outten, Stephen
• Format: Text
• Language: English
• Published: 2021
• Subjects: Greenland; Ice Sheet; North Atlantic
• Online Access: https://doi.org/10.5194/wcd-2021-26; https://wcd.copernicus.org/preprints/wcd-2021-26/

Blocking over Greenland is known to lead to strong surface impacts, such as ice sheet melting, and a change in its future frequency can have important consequences. However, as previous studies demonstrated, climate models underestimate the blocking frequency for the historical period. Even though some improvements have recently been made, the reasons for the model biases are still unclear. This study investigates whether models with realistic Greenland blocking frequency have a correct representation of its dynamical drivers, most importantly, cyclonic wave breaking (CWB). Because blocking is a rare event and its representation is model-dependent, we here use a multi-model large ensemble. All of the models underestimate CWB frequency and four out of five models underestimate the frequency of Greenland blocking. Nevertheless, they all show the typical Greenland blocking features, namely a ridge with anticyclonic anomaly over Greenland and an equatorward-shifted jet over the North Atlantic. However, we find that the model with the most realistic Greenland blocking frequency, MIROC5, has the most negative CWB frequency bias. While in reanalysis CWB is an important mechanism leading to blocking formation, the link between blocking and CWB is much weaker in MIROC5, suggesting that another mechanism leads to blocking in this model. Composites over Greenland blocking days show that the present and future experiments of each model are very similar to each other in both amplitude and pattern and that there is no significant change of Greenland blocking frequency in the future. However, this result must be taken with caution since the Greenland blocking driver is not well represented in all models. This highlights the need to accurately understand and represent the mechanisms leading to blocking formation and maintenance in models to get more reliable future projections.