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

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

doi:https://doi.org/10.5194/wcd-3-977-2022

Articles | Volume 3, issue 3

https://doi.org/10.5194/wcd-3-977-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wcd-3-977-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 3, issue 3

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

Supplement

https://doi.org/10.5194/wcd-3-977-2022-supplement

Data sets

The Subseasonal to Seasonal (S2S) Prediction Project Database (https://apps.ecmwf.int/datasets/data/s2s/) F. Vitart, C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Déqué, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H.-S. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A. W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D.-J. Won, H. Xiao, R. Zaripov, and L. Zhang https://doi.org/10.1175/BAMS-D-16-0017.1

GEFSv12 Reforecast Dataset for Supporting Subseasonal and Hydrometeorological Applications (https://registry.opendata.aws/noaa-gefs-reforecast/) H. Guan, Y. Zhu, E. Sinsky, B. Fu, W. Li, X. Zhou, X. Xue, D. Hou, J. Peng, M. M. Nageswararao, V. Tallapgragada, T. M. Hamill, J. S. Whitaker, P. Pegion, S. Frederick, M. Rosencrans, and A. Kumar https://doi.org/10.1175/MWR-D-21-0245.1

A Subseasonal Earth System Prediction Framework with CESM2 (https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.s2s_hindcasts.html, https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2-waccm.s2s_hindcasts.html) J. H. Richter, A. A. Glanville, J. Edwards, B. Kauffman, N. A. Davis, A. Jaye, H. Kim, N. M. Pedatella, L. Sun, J. Berner, W. M. Kim, S. G. Yeager, G. Danabasoglu, J. M. Caron, and K. W. Oleson https://doi.org/10.1175/WAF-D-21-0163.1

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.