Articles | Volume 3, issue 3
https://doi.org/10.5194/wcd-3-977-2022
https://doi.org/10.5194/wcd-3-977-2022
Research article
 | 
19 Aug 2022
Research article |  | 19 Aug 2022

Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems

Zachary D. Lawrence, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Amy H. Butler, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Daniela I. V. Domeisen, Etienne Dunn-Sigouin, Javier García-Serrano, Chaim I. Garfinkel, Neil P. Hindley, Liwei Jia, Martin Jucker, Alexey Y. Karpechko, Hera Kim, Andrea L. Lang, Simon H. Lee, Pu Lin, Marisol Osman, Froila M. Palmeiro, Judith Perlwitz, Inna Polichtchouk, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Irene Erner, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu

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Cited articles

Afargan-Gerstman, H. and Domeisen, D. I. V.: Pacific Modulation of the North Atlantic Storm Track Response to Sudden Stratospheric Warming Events, Geophys. Res. Lett., 47, e2019GL085007, https://doi.org/10.1029/2019GL085007, 2020. a
Ambaum, M. H. P. and Hoskins, B. J.: The NAO Troposphere–Stratosphere Connection, J. Climate, 15, 1969–1978, https://doi.org/10.1175/1520-0442(2002)015<1969:TNTSC>2.0.CO;2, 2002. a
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Anstey, J. A. and Shepherd, T. G.: High-Latitude Influence of the Quasi-Biennial Oscillation, Q. J. Roy. Meteor. Soc., 140, 1–21, https://doi.org/10.1002/qj.2132, 2014. a, b
Anstey, J. A., Simpson, I. R., Richter, J. H., Naoe, H., Taguchi, M., Serva, F., Gray, L. J., Butchart, N., Hamilton, K., Osprey, S., Bellprat, O., Braesicke, P., Bushell, A. C., Cagnazzo, C., Chen, C.-C., Chun, H.-Y., Garcia, R. R., Holt, L., Kawatani, Y., Kerzenmacher, T., Kim, Y.-H., Lott, F., McLandress, C., Scinocca, J., Stockdale, T. N., Versick, S., Watanabe, S., Yoshida, K., and Yukimoto, S.: Teleconnections of the Quasi-Biennial Oscillation in a Multi-Model Ensemble of QBO-resolving Models, Q. J. Roy. Meteor. Soc., 148, 1568–1592, https://doi.org/10.1002/qj.4048, 2022. a, b
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Short summary
Forecast models that are used to predict weather often struggle to represent the Earth’s stratosphere. This may impact their ability to predict surface weather weeks in advance, on subseasonal-to-seasonal (S2S) timescales. We use data from many S2S forecast systems to characterize and compare the stratospheric biases present in such forecast models. These models have many similar stratospheric biases, but they tend to be worse in systems with low model tops located within the stratosphere.