Articles | Volume 2, issue 3
https://doi.org/10.5194/wcd-2-913-2021
© Author(s) 2021. 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-2-913-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Minimal impact of model biases on Northern Hemisphere El Niño–Southern Oscillation teleconnections
Nicholas L. Tyrrell
CORRESPONDING AUTHOR
Meteorological Research Unit, Finnish Meteorological Institute, Helsinki, 00500, Finland
Alexey Yu. Karpechko
Meteorological Research Unit, Finnish Meteorological Institute, Helsinki, 00500, Finland
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Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
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Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
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El Niño events are known to effect the variability of the wintertime stratospheric polar vortex. The observed relationship differs from what is seen in climate models. Climate models have errors in their average winds and temperature, and in this work we artificially reduce those errors to see how that changes the communication of El Niño events to the polar stratosphere. We find reducing errors improves stratospheric variability, but does not explain the differences with observations.
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Chaim I. Garfinkel, Zachary D. Lawrence, Amy H. Butler, Etienne Dunn-Sigouin, Irene Erner, Alexey Y. Karpechko, Gerbrand Koren, Marta Abalos, Blanca Ayarzagüena, David Barriopedro, Natalia Calvo, Alvaro de la Cámara, Andrew Charlton-Perez, Judah Cohen, Daniela I. V. Domeisen, Javier García-Serrano, Neil P. Hindley, Martin Jucker, Hera Kim, Robert W. Lee, Simon H. Lee, Marisol Osman, Froila M. Palmeiro, Inna Polichtchouk, Jian Rao, Jadwiga H. Richter, Chen Schwartz, Seok-Woo Son, Masakazu Taguchi, Nicholas L. Tyrrell, Corwin J. Wright, and Rachel W.-Y. Wu
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Variability in the extratropical stratosphere and troposphere is coupled, and because of the longer timescales characteristic of the stratosphere, this allows for a window of opportunity for surface prediction. This paper assesses whether models used for operational prediction capture these coupling processes accurately. We find that most processes are too weak; however downward coupling from the lower stratosphere to the near surface is too strong.
Xavier J. Levine, Ryan S. Williams, Gareth Marshall, Andrew Orr, Lise Seland Graff, Dörthe Handorf, Alexey Karpechko, Raphael Köhler, René R. Wijngaard, Nadine Johnston, Hanna Lee, Lars Nieradzik, and Priscilla A. Mooney
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Tereza Uhlíková, Timo Vihma, Alexey Yu Karpechko, and Petteri Uotila
The Cryosphere, 18, 957–976, https://doi.org/10.5194/tc-18-957-2024, https://doi.org/10.5194/tc-18-957-2024, 2024
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A prerequisite for understanding the local, regional, and hemispherical impacts of Arctic sea-ice decline on the atmosphere is to quantify the effects of sea-ice concentration (SIC) on the sensible and latent heat fluxes in the Arctic. We analyse these effects utilising four data sets called atmospheric reanalyses, and we evaluate uncertainties in these effects arising from inter-reanalysis differences in SIC and in the sensitivity of the latent and sensible heat fluxes to SIC.
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
Weather Clim. Dynam., 3, 977–1001, https://doi.org/10.5194/wcd-3-977-2022, https://doi.org/10.5194/wcd-3-977-2022, 2022
Short summary
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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.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
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Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Nicholas L. Tyrrell, Juho M. Koskentausta, and Alexey Yu. Karpechko
Weather Clim. Dynam., 3, 45–58, https://doi.org/10.5194/wcd-3-45-2022, https://doi.org/10.5194/wcd-3-45-2022, 2022
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El Niño events are known to effect the variability of the wintertime stratospheric polar vortex. The observed relationship differs from what is seen in climate models. Climate models have errors in their average winds and temperature, and in this work we artificially reduce those errors to see how that changes the communication of El Niño events to the polar stratosphere. We find reducing errors improves stratospheric variability, but does not explain the differences with observations.
Irene Erner, Alexey Y. Karpechko, and Heikki J. Järvinen
Weather Clim. Dynam., 1, 657–674, https://doi.org/10.5194/wcd-1-657-2020, https://doi.org/10.5194/wcd-1-657-2020, 2020
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In this paper we investigate the role of the tropospheric forcing in the occurrence of the sudden stratospheric warming (SSW) that took place in February 2018, its predictability and teleconnection with the Madden–Julian oscillation (MJO) by analysing the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast. The purpose of the paper is to present the results of the analysis of the atmospheric circulation before and during the SSW and clarify the driving mechanisms.
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Short summary
Tropical Pacific sea surface temperatures (El Niño) affect the global climate. The Pacific-to-Europe connection relies on interactions of large atmospheric waves with winds and surface pressure. We looked at how mean errors in a climate model affect its ability to simulate the Pacific-to-Europe connection. We found that even large errors in the seasonal winds did not affect the response of the model to an El Niño event, which is good news for seasonal forecasts which rely on these connections.
Tropical Pacific sea surface temperatures (El Niño) affect the global climate. The...