RT Journal Article
T1 Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems
A1 Lawrence, Zachary D.
A1 Ábalos Álvarez, Marta
A1 Ayarzagüena Porras, Blanca
A1 Barriopedro Cepero, David
A1 Calvo Fernández, Natalia
A1 De La Cámara Illescas, Álvaro
A1 Rachel W.-Y. Wu, 
AB 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.
PB European Geosciences Union
YR 2022
FD 2022-08-19
LK https://hdl.handle.net/20.500.14352/108229
UL https://hdl.handle.net/20.500.14352/108229
LA eng
NO Lawrence, Z. D., Abalos, M., Ayarzagüena, B., Barriopedro, D., Butler, A. H., Calvo, N., ... & Wu, R. W. Y. (2022). Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems. Weather and Climate Dynamics Discussions, 2022, 1-37.
NO Artículo firmado por 32 autores
NO World Climate Research Programme (WCRP)
NO Stratospheric Network for the Assessment of Predictability (SNAP)
NO UK Research & Innovation (UKRI) Natural Environment Research Council (NERC)
NO Eidgenössische Technische Hochschule Zürich
NO Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
NO Israel Science Foundation
NO Universidad de Buenos Aires
NO Agencia Nacional de Promoción Científica y Tecnológica
NO Ministerio de Ciencia e Innovación (España)
NO Ministerio de Economía y Competitividad (España)
NO Natural Environment Research Council
NO Royal Society
NO National Research Foundation of Korea
NO Department of Energy, Labor and Economic Growth
NO Division of Atmospheric and Geospace Sciences
NO National Oceanic and Atmospheric Administration
DS Docta Complutense
RD 6 oct 2024