Preprints
https://doi.org/10.5194/wcd-2022-12
https://doi.org/10.5194/wcd-2022-12
 
03 Mar 2022
03 Mar 2022
Status: a revised version of this preprint is currently under review for the journal WCD.

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

Zachary D. Lawrence1,2, Marta Abalos3, Blanca Ayarzagüena3, David Barriopedro3, Amy H. Butler4, Natalia Calvo3, Alvaro de la Cámara3, Andrew Charlton-Perez5, Daniela I. V. Domeisen6,7, Etienne Dunn-Sigouin8, Javier García-Serrano9, Chaim I. Garfinkel10, Neil P. Hindley11, Liwei Jia12,13, Martin Jucker14, Alexey Y. Karpechko15, Hera Kim16, Andrea L. Lang17, Simon H. Lee18, Pu Lin13,19, Marisol Osman20,21, Froila M. Palmeiro9, Judith Perlwitz2, Inna Polichtchouk22, Jadwiga H. Richter23, Chen Schwartz10, Seok-Woo Son16, Irina Statnaia15, Masakazu Taguchi24, Nicholas L. Tyrrell15, Corwin J. Wright11, and Rachel W.-Y. Wu7 Zachary D. Lawrence et al.
  • 1Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
  • 2NOAA Physical Sciences Laboratory (PSL), Boulder, CO, USA
  • 3Department of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain
  • 4NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA
  • 5Department of Meteorology, University of Reading, Reading, UK
  • 6University of Lausanne, Lausanne, Switzerland
  • 7ETH Zurich, Zurich, Switzerland
  • 8NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
  • 9Group of Meteorology, Universitat de Barcelona (UB), Barcelona, Spain
  • 10Fredy & Nadine Herrmann Institute of Earth Sciences, The Hebrew University of Jerusalem, Israel
  • 11Centre for Space, Atmospheric and Oceanic Science, University of Bath, Bath, UK
  • 12University Corporation for Atmospheric Research, Boulder, CO
  • 13Geophysical Fluid Dynamics Laboratory, NOAA, Princeton, NJ
  • 14Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
  • 15Finnish Meteorological Institute, Meteorological Research, Helsinki, Finland
  • 16School of Earth and Environmental Sciences, Seoul National University, South Korea
  • 17Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York
  • 18Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY
  • 19Program in Atmospheric Science, Princeton University, Princeton, NJ
  • 20CONICET – Universidad de Buenos Aires, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina
  • 21Institute of Meteorology and Climate Research (IMK-TRO), Department Troposphere Research, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 22European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 23Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO
  • 24Department of Earth Science, Aichi University of Education, Kariya, Japan

Abstract. The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems.

It is found that many of the forecast systems considered exhibit warm global mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper troposphere/lower stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme vortex event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability of the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems.

These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for sub-seasonal to seasonal forecast systems, and further our understanding of the role of the stratosphere for predictive skill in the troposphere.

Zachary D. Lawrence et al.

Status: final response (author comments only)

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

Zachary D. Lawrence et al.

Zachary D. Lawrence et al.

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Latest update: 26 Jun 2022
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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.