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2. Explaining Extreme Events of 2020 from a Climate Perspective

Author

Stone, Dáithí A., Lawal, Kamoru A., Lennard, Chris, Tadross, Mark, Wolski, Piotr, Wehner, Michael F., Hope, Pandora, Zhao, Mei, Abhik, S., Tolhurst, Gen, McKay, Roseanna C., Rauniyar, Surendra P., Bettio, Lynette, Ramchurn, Avijeet, Lim, Eun-Pa, Pepler, Acacia S., Cowan, Tim, Watkins, Andrew B., Barsugli, Joseph J., Easterling, David R., Arndt, Derek S., Coates, David A., Delworth, Thomas L., Hoerling, Martin P., Johnson, Nathaniel, Kapnick, Sarah B., Kumar, Arun, Kunkel, Kenneth E., Schreck, Carl J., Vose, Russell S., Zhang, Tao, Otto, Friederike E. L., Kew, Sarah, Philip, Sjoukje, Stott, Peter, Van Oldenborgh, Geert Jan, Hoell, Andrew, Quan, Xiao-Wei, Hoerling, Martin, Fu, Rong, Mankin, Justin, Simpson, Isla, Seager, Richard, He, Cenlin, Lehner, Flavio, Lisonbee, Joel, Livneh, Ben, Sheffield, Amanda, Christidis, Nikolaos, Stott, Peter A., Kam, Jonghun, Min, Seung-Ki, Kim, Yeon-Hee, Kim, Byeong-Hee, Kug, Jong-Seong, Liu, Zhongwei, Eden, Jonathan M., Dieppois, Bastien, Drobyshev, Igor, Gallo, Carolina, Blackett, Matthew, Liu, Yujia, Li, Chao, Sun, Ying, Zwiers, Francis, Zhang, Xuebin, Jiang, Zhihong, Zheng, Fei, Pei, Lin, Yan, Zhongwei, Chen, Deliang, Miao, Shiguang, Yu, Hongyong, Yu, Xiaojing, Zhou, Ziwei, Wang, Yu, Li, Yingxin, Nanding, Nergui, Freychet, Nicolas, Dong, Buwen, Wang, Dongqian, Lott, Fraser C., Tett, Simon F. B., Sparrow, Sarah, Du, Jizeng, Fu, Kaiqi, Wang, Kaicun, Cui, Baoshan, Ma, Yuanyuan, Hu, Zhiyuan, Meng, Xianhong, Liu, Fei, Dong, Wenjie, Tang, Haosu, Wang, Ziyue, Tang, Bin, Ma, Yayu, Tian, Fangxing, Wang, Jun, Jo, Seo-Young, Seong, Min-Gyu, Son, Seok-Woo, Byun, Young-Hwa, Lu, Chunhui, Tradowsky, Jordis S., Bird, Leroy, Kreft, Peter V., Rosier, Suzanne M., Soltanzadeh, Iman, Bodeker, Greg E., Qian, Cheng, Ye, Yangbo, Zhang, Wenxia, Zhou, Tianjun, Kawase, Hiroaki, Watanabe, Shunichi, Hirockawa, Yasutaka, Imada, Yukiko, Luo, Fu, Wang, Shanshan, He, Yongli, Wang, Yanxin, and Wang, Hao

Published
2022

3. Corrigendum

Author

Lim, Eun-Pa, Hendon, Harry H., Butler, Amy H., Thompson, David W.J., Lawrence, Zachary D., Scaife, Adam A., Shepherd, Theodore G., Polichtchouk, Inna, Nakamura, Hisashi, Kobayashi, Chiaki, Comer, Ruth, Coy, Lawrence, Dowdy, Andrew, Garreaud, Rene D., Newman, Paul A., and Wang, Guomin

Subjects
Business, Earth sciences
Abstract

The authors of Lim et al. (2021) have found an error in the code that processed the data displayed in Fig. 2c in the article. The bar graph is supposed [...]

Published
2024
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4. EXPLAINING EXTREME EVENTS OF 2016 : From A Climate Perspective

Author

Herring, Stephanie C., Christidi, Nikolaos, Hoell, Andrew, Kossin, James P., Schreck, Carl J., Stott, Peter A., Webb, Robert S., Werner, Francisco E., Knutson, Thomas R., Kam, Jonghun, Zeng, Fanrong, Wittenberg, Andrew T., Brainard, Russell E., Oliver, Thomas, McPhaden, Michael J., Cohen, Anne, Venegas, Robert o, Heenan, Adel, Vargas-Ángel, Bernardo, Rotjan, Randi, Mangubhai, Sangeeta, Flint, Elizabeth, Hunter, Susan A., Oliver, Eric C. J., Perkins-Kirkpatrick, Sarah E., Holbrook, Neil J., Bindoff, Nathaniel L., Tett, Simon F. B., Falk, Alexander, Rogers, Megan, Spuler, Fiona, Turner, Calum, Wainwright, Joshua, Dimdore-Miles, Oscar, Knight, Sam, Freychet, Nicolas, Mineter, Michael J., Lehmann, Caroline E. R., Martins, Eduardo S. P. R., Coelho, Caio A. S., Haarsma, Rein, Otto, Friederike E. L., King, Andrew D., van Oldenborgh, Geert Jan, Kew, Sarah, Philip, Sjoukje, Vasconcelos, Francisco C., Cullen, Heidi, Jézéquel, Aglaé, Yiou, Pascal, Radanovics, Sabine, Vautard, Robert, Sippel, Sebastian, El-Madany, Tarek S., Migliavacca, Mirco, Mahecha, Miguel D., Carrara, Arnaud, Flach, Milan, Kaminski, Thomas, Otto, Friederike E. L., Thonicke, Kirsten, Vossbeck, Michael, Reichstein, Markus, Yuan, Xing, Wang, Linying, Wood, Eric F., Funk, Chris, Davenport, Frank, Harrison, Laura, Magadzire, Tamuka, Galu, Gideon, Artan, Guleid A., Shukla, Shraddhanand, Korecha, Diriba, Indeje, Matayo, Pomposi, Catherine, Macharia, Denis, Husak, Gregory, Nsadisa, Faka Dieudonne, Imada, Yukiko, Shiogama, Hideo, Takahashi, Chiharu, Watanabe, Masahiro, Mori, Masato, Kamae, Youichi, Maeda, Shuhei, Sun, Qiaohong, Miao, Chiyuan, Zhou, Chunlüe, Wang, Kaicun, Qi, Dan, Yuan, Xing, Wang, Shanshan, Hu, Zeng-Zhen, Qian, Cheng, Wang, Jun, Dong, Siyan, Yin, Hong, Burke, Claire, Ciavarella, Andrew, Dong, Buwen, Freychet, Nicolas, Lott, Fraser C., Tett, Simon F. B., Sun, Ying, Hu, Ting, Zhang, Xuebin, Wan, Hui, Stott, Peter, Lu, Chunhui, Christidis, Nikolaos, Manomaiphiboon, Kasemsan, Ciavarella, Andrew, Stott, Peter A., Hope, Pandora, Lim, Eun-Pa, Hendon, Harry, Wang, Guomin, Lewis, Sophie C., Mallela, Jennie, Grose, Michael R., Black, Mitchell, Risbey, James S., Uhe, Peter, Hope, Pandora K., Haustein, Karsten, Mitchell, Dann, Stott, Peter A., Christidis, Nikos, Herring, Stephanie C., Hoell, Andrew, Kossin, James P., and Schreck, Carl J.

Published
2018

10. Predictability of the 2020 Strong Vortex in the Antarctic Stratosphere and the Role of Ozone.

Author

Lim, Eun‐Pa, Zhou, Linjing, Young, Griffith, Abhik, S., Rudeva, Irina, Hope, Pandora, Wheeler, Matthew C., Arblaster, Julie M., Hendon, Harry H., Manney, Gloria L., Son, Seok‐Woo, Oh, Jiyoung, and Garreaud, René D.

Subjects
ANTARCTIC oscillation, LONG-range weather forecasting, RAINFALL anomalies, SPRING, CLIMATE change, POLAR vortex
Abstract

The Antarctic vortex of October–December 2020 was the strongest on record in the satellite era for the season in the mid‐ to lower stratosphere. However, it was poorly predicted by the Australian Bureau of Meteorology's operational seasonal climate forecast system of that time, ACCESS‐S1, even at a short lead time of a month. Using the current operational forecast system, ACCESS‐S2, we have, therefore, tried to find a primary cause of the limited predictability of this event and conducted forecast sensitivity experiments to understand the potential role of ozone in the event and its associated anomalies of the Southern Annular Mode (SAM) and rainfall over south–eastern Australia and western Patagonia. Here, we show that the 2020 strong vortex event did not follow the canonical dynamical evolution seen in previous strong vortex events in spring but suddenly appeared as a result of the record‐low upward propagating wave activity in September 2020. The ACCESS‐S2 forecasts significantly underestimated the negative wave forcing in September even at zero lead time, irrespective of the ozone configuration, therefore falling short in predicting the record strength of the polar vortex in late spring 2020. Nevertheless, ACCESS‐S2 with prescribed realistic ozone that had large anomalies in the Antarctic stratosphere significantly better predicted the strong vortex and the subsequent positive SAM and related rainfall anomalies over south–eastern Australia and western Patagonia in the austral summer of 2020–21. This highlights the potentially important role of ozone variations for seasonal climate forecasting as a source of long‐lead predictability. Plain Language Summary: The Antarctic vortex of October–December 2020 was the strongest on record in the satellite observation era for that season when monitored at 60°S in the mid‐ to lower stratosphere (altitudes of 15–30 km). However, this super vortex event was poorly predicted by the Australian Bureau of Meteorology (BoM)'s seasonal climate forecast system even at 1‐month lead time. We argue that the 2020 strong vortex was likely caused by an abrupt reduction in the upward propagating wave activity from the troposphere in September 2020, which left the stratospheric vortex undisturbed and strong. The BoM seasonal forecast system substantially underpredicted the negative wave forcing in September 2020, resulting in a poor forecast performance for the vortex strength in the following season. Forecast experiments prescribed with observed versus climatological ozone concentrations further show that using the observed ozone, characterized by a significant loss over Antarctica in spring 2020, improved the ensemble mean forecasts for the 2020 vortex strength by ∼10%–20% at different levels of the stratosphere and the associated surface climate features such as the poleward shift of the Southern Hemisphere midlatitude jet and anomalously high rainfall over south–eastern Australia and low rainfall over western Patagonia in the following summer. Key Points: The Antarctic vortex of 2020 was the strongest event in the satellite era for the October–December mean in the mid‐ to lower stratosphereSignificant lack of tropospheric wave forcing in September 2020 was responsible for the sudden appearance of the record strong polar vortexImposing realistic ozone concentrations significantly improved forecasts of the 2020 polar vortex strength and its downward impact [ABSTRACT FROM AUTHOR]

Published
2024
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13. The role of the stratosphere in subseasonal to seasonal prediction: 2. Predictability arising from stratosphere‐troposphere coupling

Author

Domeisen, Daniela I. V., Butler, Amy H., Charlton‐Perez, Andrew J., Ayarzagüena Porras, Blanca, Baldwin, Mark P., Dunn‐Sigouin, Etienne, Furtado, Jason C., Garfinkel, Chaim I., Hitchcock, Peter, Karpechko, Alexey Yu., Kim, Hera, Knight, Jeff, Lang, Andrea L., Lim, Eun‐Pa, Marshall, Andrew, Roff, Greg, Schwartz, Chen, Simpson, Isla R., Son, Seok‐Woo, Taguchi, Masakazu, Domeisen, Daniela I. V., Butler, Amy H., Charlton‐Perez, Andrew J., Ayarzagüena Porras, Blanca, Baldwin, Mark P., Dunn‐Sigouin, Etienne, Furtado, Jason C., Garfinkel, Chaim I., Hitchcock, Peter, Karpechko, Alexey Yu., Kim, Hera, Knight, Jeff, Lang, Andrea L., Lim, Eun‐Pa, Marshall, Andrew, Roff, Greg, Schwartz, Chen, Simpson, Isla R., Son, Seok‐Woo, and Taguchi, Masakazu

Abstract

The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions., Academy of Finland (Suomen Akatemia), European Council, JSPS, National Research Foundation of Korea, National Science Foundation (NSF), Swiss National Science Foundation, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

Published
2024

16. DYNAMICS AND PREDICTABILITY OF EL NIÑO–SOUTHERN OSCILLATION : An Australian Perspective on Progress and Challenges

Author

Santoso, Agus, Hendon, Harry, Watkins, Andrew, Power, Scott, Dommenget, Dietmar, England, Matthew H., Frankcombe, Leela, Holbrook, Neil J., Holmes, Ryan, Hope, Pandora, Lim, Eun-Pa, Luo, Jing-Jia, McGregor, Shayne, Neske, Sonja, Nguyen, Hanh, Pepler, Acacia, Rashid, Harun, Gupta, Alex Sen, Taschetto, Andréa S., Wang, Guomin, Abellán, Esteban, Sullivan, Arnold, Huguenin, Maurice F., Gamble, Felicity, and Delage, Francois

Published
2019

17. EXPLAINING EXTREME EVENTS OF 2017 : From A Climate Perspective

Author

Herring, Stephanie C., Christidis, Nikolaos, Hoell, Andrew, Hoerling, Marty, Stott, Peter A., Owen, Rebecca, Vano, Julie A., Dettinger, Michael D., Cifelli, Rob, Curtis, David, Dufour, Alexis, Miller, Kathleen, Olsen, J. Rolf, Wilson, Anna M., Nerem, R. S., Fasullo, J., Hoell, Andrew, Perlwitz, Judith, Dewes, Candida, Wolter, Klaus, Rangwala, Imtiaz, Quan, Xiao-Wei, Eischeid, Jon, Wang, Hailan, Schubert, Siegfried D., Koster, Randal D., Chang, Yehui, Christidis, Nikolaos, Betts, Richard A., Stott, Peter A., de Abreu, Rafael C., Cunningham, Christopher, Rudorff, Conrado M., Rudorff, Natalia, Abatan, Abayomi A., Dong, Buwen, Lott, Fraser C., Tett, Simon F. B., Sparrow, Sarah N., Navarro, Juan C. Acosta, Ortega, Pablo, García-Serrano, Javier, Guemas, Virginie, Tourigny, Etienne, Cruz-García, Rubén, Massonnet, François, Doblas-Reyes, Francisco J., Kew, Sarah F., Philip, Sjoukje Y., van Oldenborgh, Geert Jan, Otto, Friederike E. L., Vautard, Robert, van der Schrier, Gerard, Funk, Chris, Hoell, Andrew, Nicholson, Sharon, Korecha, Diriba, Galu, Gideon, Artan, Guleid, Teshome, Fetene, Hailermariam, Kinfe, Segele, Zewdu, Harrison, Laura, Tadege, Abebe, Atheru, Zachary, Pomposi, Catherine, Pedreros, Diego, Rimi, Ruksana H., Haustein, Karsten, Barbour, Emily J., Allen, Myles R., Takahashi, Chiharu, Shiogama, Hideo, Imada, Yukiko, Kosaka, Yu, Mori, Masato, Arai, Miki, Kamae, Youichi, Watanabe, Masahiro, Min, Seung-Ki, Kim, Yeon-Hee, Park, In-Hong, Lee, Donghyun, Sparrow, Sarah, Wallom, David, Stone, Dáithí, Sun, Ying, Dong, Siyan, Zhang, Xuebin, Stott, Peter, Hu, Ting, Wang, Shanshan, Yuan, Xing, Wu, Renguang, Chen, Yang, Chen, Wei, Su, Qin, Luo, Feifei, Sparrow, Sarah, Tian, Fangxing, Dong, Buwen, Tett, Simon F. B., Lott, Fraser C., Wallom, David, Zhou, Chunlüe, Wang, Kaicun, Qi, Dan, Tan, Jianguo, Perkins-Kirkpatrick, S. E., King, A. D., Cougnon, E. A., Grose, M. R., Oliver, E. C. J., Holbrook, N. J., Lewis, S. C., Pourasghar, F., Hope, Pandora, Black, Mitchell T., Lim, Eun-Pa, Dowdy, Andrew, Wang, Guomin, Pepler, Acacia S., and Fawcett, Robert J. B.

Published
2019

21. ON DETERMINING THE IMPACT OF INCREASING ATMOSPHERIC C[O.sub.2] ON THE RECORD FIRE WEATHER IN EASTERN AUSTRALIA IN FEBRUARY 2017

Author

Hope, Pandora, Black, Mitchell T., Lim, Eun-Pa, Dowdy, Andrew, Wang, Guomin, Pepler, Acacia S., and Fawcett, Robert J.B.

Subjects
Australia -- Environmental aspects, Global warming -- Influence, Wildfires -- Environmental aspects, Atmospheric carbon dioxide -- Influence, Business, Earth sciences
Abstract

February 2017 saw a broad region with record fire weather across central-eastern Australia. A hybrid attribution technique using modified observations and a seasonal forecast framework did not give a clear [...]

Published
2019
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27. 26. THE EFFECT OF INCREASING C[O.sub.2] ON THE EXTREME SEPTEMBER 2016 RAINFALL ACROSS SOUTHEASTERN AUSTRALIA: The effect of increasing atmospheric C[O.sub.2] on the extreme September 2016 rainfall across southeastern Australia was minimal, with changes in circulation and static stability driving a tendency towards drier conditions

Author

Hope, Pandora, Lim, Eun-Pa, Hendon, Harry, and Wang, Guomin

Subjects
Rain -- Forecasts and trends, Rainfall intensity duration frequencies -- Forecasts and trends, Extreme weather -- Forecasts and trends, Atmospheric carbon dioxide -- Forecasts and trends -- Environmental aspects, Market trend/market analysis, Business, Earth sciences
Abstract

Introduction. In 2016 Australia experienced extreme, widespread rainfall, with flooding and wild weather impacting some agriculture and power generation. It was particularly wet through winter and into early spring, with [...]

Published
2018
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33. ACCESS-S2: the upgraded Bureau of Meteorology multi-week to seasonal prediction system

Author

Wedd, Robin, primary, Alves, Oscar, additional, de Burgh-Day, Catherine, additional, Down, Christopher, additional, Griffiths, Morwenna, additional, Hendon, Harry H., additional, Hudson, Debra, additional, Li, Shuhua, additional, Lim, Eun-Pa, additional, Marshall, Andrew G., additional, Shi, Li, additional, Smith, Paul, additional, Smith, Grant, additional, Spillman, Claire M., additional, Wang, Guomin, additional, Wheeler, Matthew C., additional, Yan, Hailin, additional, Yin, Yonghong, additional, Young, Griffith, additional, Zhao, Mei, additional, Xiao, Yi, additional, and Zhou, Xiaobing, additional

Published
2022
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37. Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): a protocol for investigating the role of stratospheric polar vortex disturbances in subseasonal to seasonal forecasts

Author

Hitchcock, Peter, primary, Butler, Amy, additional, Charlton-Perez, Andrew, additional, Garfinkel, Chaim I., additional, Stockdale, Tim, additional, Anstey, James, additional, Mitchell, Dann, additional, Domeisen, Daniela I. V., additional, Wu, Tongwen, additional, Lu, Yixiong, additional, Mastrangelo, Daniele, additional, Malguzzi, Piero, additional, Lin, Hai, additional, Muncaster, Ryan, additional, Merryfield, Bill, additional, Sigmond, Michael, additional, Xiang, Baoqiang, additional, Jia, Liwei, additional, Hyun, Yu-Kyung, additional, Oh, Jiyoung, additional, Specq, Damien, additional, Simpson, Isla R., additional, Richter, Jadwiga H., additional, Barton, Cory, additional, Knight, Jeff, additional, Lim, Eun-Pa, additional, and Hendon, Harry, additional

Published
2022
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40. Subseasonal to Seasonal Climate Forecasts Provide the Backbone of a Near-Real-Time Event Explainer Service

Author

Hope, Pandora, primary, Zhao, Mei, additional, Abhik, S., additional, Tolhurst, Gen, additional, McKay, Roseanna C., additional, Rauniyar, Surendra P., additional, Bettio, Lynette, additional, Ramchurn, Avijeet, additional, Lim, Eun-Pa, additional, Pepler, Acacia S., additional, Cowan, Tim, additional, and Watkins, Andrew B., additional

Published
2022
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41. Long-range prediction and the stratosphere

Author

Scaife, Adam A., primary, Baldwin, Mark P., additional, Butler, Amy H., additional, Charlton-Perez, Andrew J., additional, Domeisen, Daniela I. V., additional, Garfinkel, Chaim I., additional, Hardiman, Steven C., additional, Haynes, Peter, additional, Karpechko, Alexey Yu, additional, Lim, Eun-Pa, additional, Noguchi, Shunsuke, additional, Perlwitz, Judith, additional, Polvani, Lorenzo, additional, Richter, Jadwiga H., additional, Scinocca, John, additional, Sigmond, Michael, additional, Shepherd, Theodore G., additional, Son, Seok-Woo, additional, and Thompson, David W. J., additional

Published
2022
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42. Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts

Author

Hitchcock, Peter, primary, Butler, Amy, additional, Charlton-Perez, Andrew, additional, Garfinkel, Chaim, additional, Stockdale, Tim, additional, Anstey, James, additional, Mitchell, Dann, additional, Domeisen, Daniela I. V., additional, Wu, Tongwen, additional, Lu, Yixiong, additional, Mastrangelo, Daniele, additional, Malguzzi, Piero, additional, Lin, Hai, additional, Muncaster, Ryan, additional, Merryfield, Bill, additional, Sigmond, Michael, additional, Xiang, Baoqiang, additional, Jia, Liwei, additional, Hyun, Yu-Kyung, additional, Oh, Jiyong, additional, Specq, Damien, additional, Simpson, Isla R., additional, Richter, Jadwiga H., additional, Barton, Cory, additional, Knight, Jeff, additional, Lim, Eun-Pa, additional, and Hendon, Harry, additional

Published
2022
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45. Long Range Prediction and the Stratosphere

Author

Scaife, Adam A., primary, Baldwin, Mark P., additional, Butler, Amy H., additional, Charlton-Perez, Andrew J., additional, Domeisen, Daniella I. V., additional, Garfinkel, Chaim I., additional, Hardiman, Steven C., additional, Haynes, Peter, additional, Karpechko, Alexey Yu, additional, Lim, Eun-Pa, additional, Noguchi, Shunsuke, additional, Perlwitz, Judith, additional, Polvani, Lorenzo, additional, Richter, Jadwiga H., additional, Scinocca, John, additional, Sigmond, Michael, additional, Shepherd, Theodore G., additional, Son, Seok-Woo, additional, and Thompson, David W. J., additional

Published
2021
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47. Corrigendum to: ACCESS-S1: The new Bureau of Meteorology multi-week to seasonal prediction system

Author

Hudson, Debra, primary, Alves, Oscar, additional, Hendon, Harry H., additional, Lim, Eun-Pa, additional, Liu, Guoqiang, additional, Luo, Jing-Jia, additional, MacLachlan, Craig, additional, Marshall, Andrew G., additional, Shi, Li, additional, Wang, Guomin, additional, Wedd, Robin, additional, Young, Griffith, additional, Zhao, Mei, additional, and Zhou, Xiaobing, additional

Published
2020
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49. The role of the stratosphere in subseasonal to seasonal prediction part I: predictability of the stratosphere

Author

Domeisen, Daniela I. V., Butler, Amy H., Charlton-Perez, Andrew J., Ayarzagüena, Blanca, Baldwin, Mark P., Dunn‐Sigouin, Etienne, Furtado, Jason C., Garfinkel, Chaim I., Hitchcock, Peter, Karpechko, Alexey Yu., Kim, Hera, Knight, Jeff, Lang, Andrea L., Lim, Eun‐Pa, Marshall, Andrew, Roff, Greg, Schwartz, Chen, Simpson, Isla R., Son, Seok‐Woo, and Taguchi, Masakazu

Abstract

The stratosphere has been identified as an important source of predictability for a range of processes on subseasonal to seasonal (S2S) timescales. Knowledge about S2S predictability within the stratosphere is however still limited. This study evaluates to what extent predictability in the extratropical stratosphere exists in hindcasts of operational prediction systems in the S2S database. The stratosphere is found to exhibit extended predictability as compared to the troposphere. Prediction systems with higher stratospheric skill tend to also exhibit higher skill in the troposphere. The analysis also includes an assessment of the predictability for stratospheric events, including early and mid‐winter sudden stratospheric warming (SSW) events, strong vortex events, and extreme heat flux events for the Northern Hemisphere, and final warming events for both hemispheres. Strong vortex events and final warming events exhibit higher levels of predictability as compared to SSW events. In general, skill is limited to the deterministic range of one to two weeks. High‐top prediction systems overall exhibit higher stratospheric prediction skill as compared to their low‐top counterparts, pointing to the important role of stratospheric representation in S2S prediction models.

Published
2020

50. The role of the stratosphere in subseasonal to seasonal prediction part II: predictability arising from stratosphere ‐ troposphere coupling

Author

Domeisen, Daniela I. V., Butler, Amy H., Charlton-Perez, Andrew J., Ayarzagüena, Blanca, Baldwin, Mark P., Dunn‐Sigouin, Etienne, Furtado, Jason C., Garfinkel, Chaim I., Hitchcock, Peter, Karpechko, Alexey Yu., Kim, Hera, Knight, Jeff, Lang, Andrea L., Lim, Eun‐Pa, Marshall, Andrew, Roff, Greg, Schwartz, Chen, Simpson, Isla R., Son, Seok‐Woo, and Taguchi, Masakazu

Abstract

The stratosphere can have a signi_cant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and tropo sphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Prob abilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Paci_c and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Nin~o Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the USA, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

Published
2020

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