Применение метода подталкивания с целью получения начальных состояний для сезонных ретроспективных прогнозов климатической модели ИВМ РАН
Авторы
-
М. А. Тарасевич
-
Е. М. Володин
Ключевые слова:
климатическая модель ИВМ РАН
метод подталкивания
ретроспективные сезонные прогнозы
инициализация
Аннотация
В данном исследовании рассматривается применение метода подталкивания в глобальной климатической модели ИВМ РАН для улучшения начальных состояний для сезонных ретроспективных прогнозов. Результаты трех климатических экспериментов с подталкиванием сравниваются с данными реанализов и ансамблем исторических экспериментов по протоколу CMIP6. Применение метода подталкивания позволяет значительно уменьшить ошибки модели в воспроизведении атмосферных и океанических полей. Эксперимент, в котором подталкивание не применяется к приземным атмосферным уровням, показывает наилучшие результаты за счет уменьшения влияния процедур подталкивания на физику пограничного слоя модели атмосферы. Проводится сравнение качества ретроспективных прогнозов на ноябрь–март, полученных с использованием различных методов инициализации. Подход с использованием начальных состояний по данным расчетов с подталкиванием превосходит другие методы инициализации для ретроспективных прогнозов на зимний сезон Северного полушария с заблаговременностью один месяц. Однако для первого месяца прогноза наилучшие результаты демонстрирует инициализация полными полями. В работе подчеркивается необходимость использования подталкивания в модели океана для обеспечения качественного прогноза с большой заблаговременностью, а также предлагается применение этого метода для инициализации климатических прогнозов на год–десятилетие.
Раздел
Методы и алгоритмы вычислительной математики и их приложения
Библиографические ссылки
- E. M. Volodin, E. V. Mortikov, S. V. Kostrykin, et al., “Simulation of the present-day climate with the climate model INMCM5,” Climate Dynamics 49 (11), 3715-3734 (2017).
doi 10.1007/s00382-017-3539-7
- V. Eyring, S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, “Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization,” Geoscientific Model Development 9 (5), 1937-1958 (2016).
doi 10.5194/gmd-9-1937-2016
- L. Bock, A. Lauer, M. Schlund, M. Barreiro, N. Bellouin, C. Jones, et al., “Quantifying progress across different CMIP phases with the ESMValTool,” J. Geophys. Research: Atmospheres 125, e2019JD032321 (2020).
doi 10.1029/2019JD032321
- Y.-H. Kim, S.-K. Min, X. Zhang, J. Sillmann, and M. Sandstad, “Evaluation of the CMIP6 multi-model ensemble for climate extreme indices,” Weather and Climate Extremes 29, 100269 (2020).
doi 10.1016/j.wace.2020.100269
- E. Volodin and A. Gritsun, “Nature of the Decrease in Global Warming at the Beginning of the 21st Century,” Dokl. Earth Sc. 482 (1), 1221-1224 (2018).
doi 10.1134/S1028334X18090210
- B. C. O’Neill, C. Tebaldi, D. P. van Vuuren, et al., “The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6,” Geoscientific Model Development 9, 3461-3482 (2016).
doi 10.5194/gmd-9-3461-2016
- M. Meinshausen, Z. R. J. Nicholls, J. Lewis, et al., “The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500,” Geoscientific Model Development 13, 3571-3605 (2020).
doi 10.5194/gmd-13-3571-2020
- C. Tebaldi, K. Debeire, V. Eyring, et al., “Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6,” Earth Syst. Dynam. 12, 253-293 (2021).
doi 10.5194/esd-12-253-2021
- W. Hazeleger, C. Severijns, T. Semmler, et al., “EC-Earth: A Seamless Earth-System Prediction Approach in Action,” Bulletin of the American Meteorological Society 91 (10), 1357-1364 (2010).
doi 10.1175/2010BAMS2877.1
- B. Hoskins, “The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science,” Q.J.R. Meteorol. Soc. 139 (672), 573-584, (2013).
doi 10.1002/qj.1991
- M. A. Tolstykh, J.-F. Geleyn, E. M. Volodin, et al., “Development of the multiscale version of the SL-AV global atmosphere model,” Russ. Meteorol. Hydrol. 40, 374-382 (2015).
doi 10.3103/S1068373915060035
- V. V. Vorobyeva and E. M. Volodin, “Experimental Studies of Seasonal Weather Predictability Based on the INM RAS Climate Model,” Mathematical Models and Computer Simulations 13 (4), 571-578 (2021).
doi 10.1134/S2070048221040232
- V. M. Khan, E. N. Kruglova, V. A. Tishchenko, et al., “Verification of Seasonal Ensemble Forecasts Based on the INM-CM5 Earth System Model,” Russ. Meteorol. Hydrol. 49, 587-597 (2024).
doi 10.3103/S1068373924070033
- V. V. Vorobeva, E. M. Volodin, A. S. Gritsun, and M. A. Tarasevich, “Analysis of the Atmosphere and the Ocean Upper Layer State Predictability for up to 5 Years Ahead Using the INMCM5 Climate Model Hindcasts,” Russ. Meteorol. Hydrol. 48 (7), 581-589 (2023).
doi 10.3103/S106837392307004X
- M. A. Tarasevich and E. M. Volodin, “The Influence of Autumn Eurasian Snow Cover on the Atmospheric Dynamics Anomalies during the Next Winter in INMCM5 Model Data,” Supercomput. Front. Innov. 8 (4), 24-39 (2021).
doi 10.14529/jsfi210403
- P. N. Vargin, V. V. Bragina, E. M. Volodin, et al., “Investigation of the Predictability of the Arctic Stratospheric Polar Vortex Variability in the INMCM5 Seasonal Predictions,” Russ. Meteorol. Hydrol. 49, 700-710 (2024).
doi 10.3103/S1068373924080053
- V. V. Bragina, M. A. Tarasevich, and E. M. Volodin, “Prediction of the Arctic Sea Ice Characteristics for Summer Seasons Using the INM RAS Earth System Model,” Russ. Meteorol. Hydrol. 49, 681-690 (2024).
doi 10.3103/S106837392408003X
- Y. D. Resnyanskii, A. A. Zelen’ko, B. S. Strukov, et al., “Assessment of the Reproducibility of Oceanographic Fields in Retrospective Forecasts Using the INM-CM5 Earth System Model,” Russ. Meteorol. Hydrol. 49, 183-194 (2024).
doi 10.3103/S1068373924030014
- L. Garcia-Oliva, F. Counillon, I. Bethke, and N. Keenlyside, “Intercomparison of initialization methods for seasonal-to-decadal climate predictions with the NorCPM,” Clim. Dyn. 62, 5425-5444 (2024).
doi 10.1007/s00382-024-07170-w
- W. J. Merryfield, J. Baehr, L. Batté, et al., “Current and Emerging Developments in Subseasonal to Decadal Prediction,” Bull. Amer. Meteor. Soc. 101 (6), E869-E896 (2020).
doi 10.1175/BAMS-D-19-0037.1
- D. M. Smith, S. Cusack, A. W. Colman, et al., “Improved Surface Temperature Prediction for the Coming Decade from a Global Climate Model’’, Science 317, 796-799 (2007).
doi 10.1126/science.1139540
- A. Carrassi, R. J. T. Weber, V. Guemas, et al., “Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations,” Nonlin. Processes Geophys. 21 (2), 521-537 (2014).
doi 10.5194/npg-21-521-2014
- H. Hersbach, B. Bell, P. Berrisford, et al., “The ERA5 global reanalysis,” Q.J.R. Meteorol. Soc. 146, 1999-2049, (2020).
doi 10.1002/qj.3803
- J. A. Carton, G. A. Chepurin, and L. Chen, “SODA3: A New Ocean Climate Reanalysis,” Journal of Climate 31 (17), 6967-6983 (2018).
doi 10.1175/JCLI-D-18-0149.1
- J. A. Carton, S. G. Penny, and E. Kalnay, “Temperature and Salinity Variability in the SODA3, ECCO4r3, and ORAS5 Ocean Reanalyses, 1993-2015’’, Journal of Climate 32 (8), 2277-2293 (2019).
doi 10.1175/JCLI-D-18-0605.1
- D. P. Mulholland, P. Laloyaux, K. Haines, et al., “Origin and Impact of Initialization Shocks in Coupled Atmosphere-Ocean Forecasts,” Monthly Weather Rev. 143 (11), 4631-4644 (2015).
doi 10.1175/MWR-D-15-0076.1
- J. E. Hoke and R. A. Anthes, “The Initialization of Numerical Models by a Dynamic-Initialization Technique,” Monthly Weather Review 104 (12), 1551-1556 (1976).
doi 10.1175/1520-0493(1976)104<1551: TIONMB>2.0.CO;2.
- R. Bilbao, S. Wild, P. Ortega, et al., “Assessment of a full-field initialized decadal climate prediction system with the CMIP6 version of EC-Earth,” Earth Syst. Dynam. 12 (1), 173-196 (2021).
doi 10.5194/esd-12-173-2021
- A. Düsterhus and S. Brune, “Decadal predictability of seasonal temperature distributions,” Geophysical Research Letters 51, e2023GL107838 (2024).
doi 10.1029/2023GL107838
- G. A. Meehl, L. Goddard, G. Boer, et al., “Decadal Climate Prediction: An Update from the Trenches,” Bull. Amer. Meteor. Soc. 95 (2), 243-267 (2014).
doi 10.1175/BAMS-D-12-00241.1
- E. Kalnay, Atmospheric Modeling, Data Assimilation and Predictability(Cambridge University Press, Cambridge, 2003).
doi 10.1017/CBO9780511802270
- APEC Climate Center.
https://apcc21.org/prediction/global/model?lang=en . Cited September 17, 2025.
- M. A. Tolstykh, R. Yu. Fadeev, V. V. Shashkin, et al., “The SLAV072L96 Model for Long-range Meteorological Forecasts,” Russ. Meteorol. Hydrol. 49, 576-586 (2024).
doi 10.3103/S1068373924070021
- W. J. Merryfield, W.-S. Lee, G. J. Boer, et al., “The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization,” Monthly Weather Rev. 141 (8), 2910-2945 (2013).
doi 10.1175/MWR-D-12-00216.1
- S. G. Penny, et al., Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations, WMO Tech. Rep. (World Meteorological Organization, 2017).
https://repository.library.noaa.gov/view/noaa/28431/. Cited September 17, 2025.
- A. J. Charlton-Perez, L. Ferranti and R. W. Lee, “The influence of the stratospheric state on North Atlantic weather regimes,” Q.J.R. Meteorol. Soc. 144, 1140-1151 (2018).
doi 10.1002/qj.3280
- J. Kidston, A. Scaife, S. Hardiman, et al., “Stratospheric influence on tropospheric jet streams, storm tracks and surface weather,” Nature Geoscience 8, 433-440 (2015).
doi 10.1038/ngeo2424
- J. W. Hurrell, “Influence of variations in extratropical wintertime teleconnections on northern hemisphere temperature,” Geophys. Res. Lett. 23 (6), 665-668 (1996).
doi 10.1029/96GL00459
- L. M. Polvani, L. Sun, A. H. Butler, et al., “Distinguishing Stratospheric Sudden Warmings from ENSO as Key Drivers of Wintertime Climate Variability over the North Atlantic and Eurasia,” Journal of Climate 30 (6), 1959-1969 (2017).
doi 10.1175/JCLI-D-16-0277.1
- E. M. Volodin and S. V. Kostrykin, “The aerosol module in the INM RAS climate model,” Russ. Meteorol. Hydrol. 41, 519-528 (2016).
doi 10.3103/S106837391608001X
- E. M. Volodin and V. N. Lykosov, “Parametrization of heat and moisture transfer in the soil-vegetation system for use in atmospheric general circulation models: 1. Formulation and simulations based on local observational data,” Izvestiya, Atmospheric and Oceanic Physics 34 (4), 405-416 (1998).
https://www.researchgate.net/publication/270586916_Parameterization_of_Heat_and_Moisture_Transfer_in_the_Soil-Vegetation_System_for_Use_in_Atmospheric_General_Circulation_Models_1_Formulation_and_Simulations_Based_on_Local_Observational_Data . Cited September 17, 2025.
- K. M. Terekhov, E. M. Volodin, and A. V. Gusev, “Methods and efficiency estimation of parallel implementation of the sigma-model of general ocean circulation,” Russ. J. Numer. Anal. Math. Modelling 26 (2), 189-208 (2011).
doi 10.1515/rjnamm.2011.011
- N. G. Yakovlev, “Reproduction of the large-scale state of water and sea ice in the Arctic Ocean in 1948-2002: Part I. Numerical Model,” Izvestiya, Atmospheric and Oceanic Physics 45 (3), 357-371 (2009).
doi 10.1134/S0001433809030098
- V. Ya. Galin, E. M. Volodin, and S. P. Smyshlyaev, “Atmospheric general circulation model of INM RAS with ozone dynamics,” Russian Meteorology and Hydrology 5, 7-15 (2003).
https://www.scopus.com/pages/publications/2442505832?inward . Cited September 17, 2025.
- G. Marchuk, Numerical Methods in Weather Prediction(Academic Press, New York-London, 1974).
doi 10.1016/B978-0-12-470650-7.X5001-4
- E. Hairer and G. Wanner, Solving Ordinary Differential Equations II. Stiff and Differential-Algebraic Problems, Ed. 2 (Springer Berlin, Heidelberg, 1996).
doi 10.1007/978-3-642-05221-7
- P. J. Telford, P. Braesicke, O. Morgenstern, and J. A. Pyle, “Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model,” Atmos. Chem. Phys. 8, 1701-1712 (2008).
doi 10.5194/acp-8-1701-2008
- N. G. Loeb, D. R. Doelling, H. Wang, et al., “Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product,” Journal of Climate 31 (2), 895-918 (2018).
doi 10.1175/JCLI-D-17-0208.1
- WMO Manual on the Global Data-processing and Forecasting System. Volume I (Annex IV to WMO Technical Regulations). Global Aspects, WMO-No. 485, Geneva, 2023.
https://library.wmo.int/idurl/4/35703 . Cited September 17, 2025.