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, Irina Statnaia, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2022-12', Anonymous Referee #1, 22 Mar 2022
  • RC2: 'Comment on wcd-2022-12', Anonymous Referee #2, 24 Apr 2022
  • AC1: 'Comment on wcd-2022-12', Zachary D Lawrence, 19 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Zachary D Lawrence on behalf of the Authors (19 Jun 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Jun 2022) by Stephan Pfahl
RR by Anonymous Referee #2 (13 Jul 2022)
ED: Publish as is (13 Jul 2022) by Stephan Pfahl
AR by Zachary D Lawrence on behalf of the Authors (21 Jul 2022)  Author's response   Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Zachary D Lawrence on behalf of the Authors (17 Aug 2022)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Aug 2022) by Stephan Pfahl
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.