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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Aug 2022
Research article |
| 19 Aug 2022
| 19 Aug 2022
Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems
Zachary D. Lawrence
CORRESPONDING AUTHOR
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
NOAA Physical Sciences Laboratory (PSL), Boulder, CO, USA
Marta Abalos
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
Blanca Ayarzagüena
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
David Barriopedro
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
Amy H. Butler
NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA
Natalia Calvo
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
Alvaro de la Cámara
Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
Andrew Charlton-Perez
Department of Meteorology, University of Reading, Reading, UK
Daniela I. V. Domeisen
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
Etienne Dunn-Sigouin
NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
Javier García-Serrano
Group of Meteorology, Universitat de Barcelona (UB), Barcelona, Spain
Chaim I. Garfinkel
Fredy & Nadine Herrmann Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Neil P. Hindley
Centre for Space, Atmospheric and Oceanic Science, University of Bath, Bath, UK
Liwei Jia
University Corporation for Atmospheric Research, Boulder, CO, USA
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Martin Jucker
Climate Change Research Centre, the University of New South Wales, Sydney, NSW, Australia
Australian Research Council Center of Excellence for Climate Extremes, Sydney, NSW, Australia
Alexey Y. Karpechko
Meteorological Research, Finnish Meteorological Institute, Helsinki, Finland
School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
Andrea L. Lang
Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, NY, USA
Simon H. Lee
Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA
Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ, USA
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Marisol Osman
Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires, Argentina
now at: Department Troposphere Research, Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Froila M. Palmeiro
Group of Meteorology, Universitat de Barcelona (UB), Barcelona, Spain
Judith Perlwitz
NOAA Physical Sciences Laboratory (PSL), Boulder, CO, USA
Inna Polichtchouk
European Centre for Medium-Range Weather Forecasts, Reading, UK
Jadwiga H. Richter
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Chen Schwartz
Fredy & Nadine Herrmann Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Seok-Woo Son
School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
Irene Erner
Meteorological Research, Finnish Meteorological Institute, Helsinki, Finland
Masakazu Taguchi
Department of Earth Science, Aichi University of Education, Kariya, Japan
Nicholas L. Tyrrell
Meteorological Research, Finnish Meteorological Institute, Helsinki, Finland
Corwin J. Wright
Centre for Space, Atmospheric and Oceanic Science, University of Bath, Bath, UK
Rachel W.-Y. Wu
Institute for Atmospheric and Climate Science, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
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Cited
11 citations as recorded by crossref.
- Ensemble size versus bias correction effects in subseasonal-to-seasonal (S2S) forecasts J. Han et al. 10.1186/s40562-023-00292-9
- Exploring the link between austral stratospheric polar vortex anomalies and surface climate in chemistry-climate models N. Bergner et al. 10.5194/acp-22-13915-2022
- Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings W. Wicker et al. 10.5194/wcd-4-81-2023
- Tropospheric links to uncertainty in stratospheric subseasonal predictions R. Wu et al. 10.5194/acp-24-12259-2024
- Determining the height of deep volcanic eruptions over the tropical western Pacific with Himawari-8 C. Lucas & S. Siems 10.1071/ES22033
- The impact of vertical model levels on the prediction of MJO teleconnections: Part I—The tropospheric pathways in the UFS global coupled model C. Zheng et al. 10.1007/s00382-024-07377-x
- Large-scale dynamic processes during the minor and major sudden stratospheric warming events in January–February 2023 P. Vargin et al. 10.1016/j.atmosres.2024.107545
- Amplified Decadal Variability of Extratropical Surface Temperatures by Stratosphere‐Troposphere Coupling A. Butler et al. 10.1029/2023GL104607
- Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere S. Phakula et al. 10.1002/met.2170
- Stratospheric modulation of Arctic Oscillation extremes as represented by extended-range ensemble forecasts J. Spaeth & T. Birner 10.5194/wcd-3-883-2022
- Which Sudden Stratospheric Warming Events Are Most Predictable? D. Chwat et al. 10.1029/2022JD037521
9 citations as recorded by crossref.
- Ensemble size versus bias correction effects in subseasonal-to-seasonal (S2S) forecasts J. Han et al. 10.1186/s40562-023-00292-9
- Exploring the link between austral stratospheric polar vortex anomalies and surface climate in chemistry-climate models N. Bergner et al. 10.5194/acp-22-13915-2022
- Increased vertical resolution in the stratosphere reveals role of gravity waves after sudden stratospheric warmings W. Wicker et al. 10.5194/wcd-4-81-2023
- Tropospheric links to uncertainty in stratospheric subseasonal predictions R. Wu et al. 10.5194/acp-24-12259-2024
- Determining the height of deep volcanic eruptions over the tropical western Pacific with Himawari-8 C. Lucas & S. Siems 10.1071/ES22033
- The impact of vertical model levels on the prediction of MJO teleconnections: Part I—The tropospheric pathways in the UFS global coupled model C. Zheng et al. 10.1007/s00382-024-07377-x
- Large-scale dynamic processes during the minor and major sudden stratospheric warming events in January–February 2023 P. Vargin et al. 10.1016/j.atmosres.2024.107545
- Amplified Decadal Variability of Extratropical Surface Temperatures by Stratosphere‐Troposphere Coupling A. Butler et al. 10.1029/2023GL104607
- Literature survey of subseasonal‐to‐seasonal predictions in the southern hemisphere S. Phakula et al. 10.1002/met.2170
Latest update: 13 Dec 2024
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.
Forecast models that are used to predict weather often struggle to represent the Earth’s...
