Predictability of the tropospheric circulation in the Southern Hemisphere from CHFP models
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An assessment of the predictability and prediction skill of the tropospheric circulation in the Southern Hemisphere was done. The analysis is based on seasonal forecasts of geopotential heights at 200, 500 and 850 hPa, for austral summer and winter from 11 models participating in the Climate Historical Forecast Project. It is found that predictability (signal-to-variance ratio) and prediction skill (anomaly correlation) in the tropics is higher than in the extratropics and is also higher in summer than in winter. Both predictability and skill are higher at high than at low altitudes. Modest values of predictability and skill are found at polar latitudes in the Bellinghausen-Amundsen Seas. The analysis of the changes in predictability and prediction skill in ENSO events reveals that both are slightly higher in the El Niño-Southern Oscillation (ENSO) years than in all years, while the spatial patterns of maxima and minima remain unchanged. Changes in signal-to-noise ratio observed are mainly due to signal changes rather than changes in noise. Composites of geopotential heights anomalies for El Niño and La Niña years are in agreement with observations.
KeywordsSouthern Hemisphere El niño southern oscillation Seasonal predictability Geopotential heights
We acknowledge the WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project (CHFP, see Kirtman and Pirani 2009) and the Centro de Investigaciones del Mar y la Atmosfera (CIMA) for providing the model output http://chfps.cima.fcen.uba.ar/DS. We also thank the data providers for making the model output available through CHFP. This research was supported by UBACyT 20020100100434, ANPCyTPICT-2010-2110, European Union’s FP7-funded SPECS (GA 308378) project and the Catalan Government. M.O. is supported by a Ph.D grant from CONICET, Argentina.
- MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P, Madec G (2014) Global seasonal forecast system version 5 (GloSea5): a high resolution seasonal forecast system. Q J R Meteorol Soc. doi: 10.1002/qj.2396. ISSN 1477-870X
- Molteni F, Stockdale T, Balmaseda M, Balsamo G, Buizza R, Ferranti L, Magnusson L, Mogensen K, Palmer T, Vitart F (2011) The new ECMWF seasonal forecast system (System 4). ECMWF Technical Memorandum 656Google Scholar
- National Research Council (2010) Assessment of intraseasonal to interannual climate prediction and predictability. The National Academies Press, Washington, p 192Google Scholar
- Palmer TN, Doblas-Reyes FJ, Hagedorn R, Alessandri A, Gualdi S, Andersen U, Feddersen H, Cantelaube P, TerresJM Davey M, Graham R, Délécluse P, Lazar A, Déqué M, GuérémyJF Díez E, Orfila B, Hoshen M, Morse AP, Keenlyside N, Latif M, Maisonnave E, Rogel P, Marletto V, Thomson MC (2004) Development of a European multi model ensemble system for seasonal-to-interannual prediction (demeter). Bull Am Meteorol Soc 85:853–872. doi: 10.1175/BAMS-85-6-853 CrossRefGoogle Scholar
- Van den Dool H (2007) Empirical methods in short-term climate prediction. Oxford University Press, Oxford, p 215Google Scholar
- World Meteorological Organization (2009) Report of the world climate conference-3 better climate information for a better future: conference statement. http://www.wmo.int/wcc3/documents/WCC-3_Statement_07-09-09_mods.pdf. Accessed 11 Aug 2014
- Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka YT, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the meteorological research institute: MRI-CGCM3 -model description and basic performance. J Meterol Soc Jpn 90A:23–64. doi: 10.2151/jmsj.2012-A02 CrossRefGoogle Scholar