Geodesic
Experimental studies of seasonal weather predictability based on the INM RAS climate model
Matematičeskoe modelirovanie, Tome 32 (2020) no. 11, pp. 47-58.

Voir la notice de l'article provenant de la source Math-Net.Ru

The technology for constructing a set of initial data using the methodology for eliminating the displacement of the model bias for conducting seasonal time-scale experiments with the climate model of Institute of Numerical Mathematics (INM) RAS originally developed for long-term experiments is presented. The comparative analysis of multiyear mean correlation coefficients of anomalies for the winter seasons of various weather fields and regions with similar results of the SLAV model was carried out. The presence of an increase in the correlation coefficients of anomalies in the years of the phenomena of El Niño and La Niña was revealed. The coincidence of the phases of quasi-biennial oscillations is shown. The model sea-level pressure, precipitation and surface temperature anomalies are compared with reanalysis anomalies. Similarity is shown.
Keywords: model, climate, weather, seasonal forecasting, anomaly correlation coefficient, atmosphere, ocean, El Niño–Southern Oscillation (ENSO), Quasi-biennial oscillation (QBO). 
@article{MM_2020_32_11_a3,
     author = {V. V. Vorobyeva and E. M. Volodin},
     title = {Experimental studies of seasonal weather predictability based on the {INM} {RAS} climate model},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {47--58},
     publisher = {mathdoc},
     volume = {32},
     number = {11},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_2020_32_11_a3/}
}
TY  - JOUR
AU  - V. V. Vorobyeva
AU  - E. M. Volodin
TI  - Experimental studies of seasonal weather predictability based on the INM RAS climate model
JO  - Matematičeskoe modelirovanie
PY  - 2020
SP  - 47
EP  - 58
VL  - 32
IS  - 11
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MM_2020_32_11_a3/
LA  - ru
ID  - MM_2020_32_11_a3
ER  - 
%0 Journal Article
%A V. V. Vorobyeva
%A E. M. Volodin
%T Experimental studies of seasonal weather predictability based on the INM RAS climate model
%J Matematičeskoe modelirovanie
%D 2020
%P 47-58
%V 32
%N 11
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MM_2020_32_11_a3/
%G ru
%F MM_2020_32_11_a3
V. V. Vorobyeva; E. M. Volodin. Experimental studies of seasonal weather predictability based on the INM RAS climate model. Matematičeskoe modelirovanie, Tome 32 (2020) no. 11, pp. 47-58. http://geodesic.mathdoc.fr/item/MM_2020_32_11_a3/

[1] B. Hoskins, “The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science”, Quarterly Journal of the Royal Meteorological Society, 139:672 (2013), 573–584 | DOI

[2] M. A. Tolstykh, Globalnaia polulagranzheva model chislennogo prognoza pogody, OAO FOP, M.–Obninsk, 2010, 111 pp.

[3] B. E. Shneerov, V. P. Meleshko i dr., “Sovremennaia versiia globalnoi modeli obshchei tsirkuliatsii atmosfery GGO (Versia MGO-2)”, Trudy GGO, 2001, no. 550, 3–43

[4] A. V. Murav'ev, V. D. Kaznacheeva, E. N. Kruglova, I. A. Kulikova, “Long-Term Forecasting of Anomalous Synoptic. Part I. Major Construction and Technological Charac-teristics of a Long-Term Dynamic-Statistical Weather Forecast Scheme”, Russ. Meteorol. Hydrol., 24:3 (1999), 18–24

[5] D. B. Kiktev, V. M. Khan, V. N. Kryzhov, R. B. Zaripov, E. N. Kruglova, I. A. Kulikova, V. A. Tishchenko, “Tekhnologiia vypuska dolgosrochnykh prognozov severo-evraziiskogo klimaticheskogo tsentra (SEAKTS)”, Trudy gidrometeorologicheskogo nauchno-issledovatelskogo tsentra RF, 2015, no. 358, 36–58

[6] D. B. Kiktev, M. A. Tolstykh, R. B. Zaripov i dr., Sovmestnye ispytaniia tekhnologii globalnogo sezonnogo prognoza na osnove modelei Gidromettsentra Rossii i GGO, Inf. sb. Rezultaty ispytaniia novykh i usovershenstvovannykh tekhnologii, modelei i metodov gidrometeorologicheskikh prognozov, 38, eds. G.K. Veselova i dr., IG SOTSIN, M.–Obninsk, 2011, 117 pp.

[7] E. M. Volodin, E. V. Mortikov, S. V. Kostrykin, V. Ya. Galin, V. N. Lykossov, A. S. Gritsun, N. A. Diansky, A. V. Gusev, N. G. Yakovlev, “Simulation of the present-day climate with the climate model INMCM5”, Clim. Dyn, 49:11-12 (2017), 3715–3734 | DOI | MR

[8] F. Molteni, T. Stockdale, M. Balmaseda et al., “The new ECMWF seasonal forecast system (System 4)”, ECMWF Technical Memorandum, 2011, no. 656, 49

[9] T. Stockdale, S. Johnson, L. Ferranti et al, “ECMWF's new long-range forecasting system SEAS5”, ECMWF Newsletter, 2018, no. 154, 15–20

[10] H. Du, F. J. Doblas-Reyes, J. García-Serrano, V. Guémas, Y. Soufflet, B. Wouters, “Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations”, Clim. Dyn., 39:7-8 (2012), 2013–2023 | DOI

[11] J. A. Carton, G. A. Chepurin, L. Chen, “SODA3: A New Ocean Climate Reanalysis”, J. Climate, 31:17 (2018), 6967–6983 | DOI