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Invited researcher Jürgen Kurths
Contract number
Time span of the project

As of 30.01.2020

Number of staff members
scientific publications
Objects of intellectual property
General information

Name of the project: New approaches to research of climatic processes and forecasting extreme phenomena

Strategy for Scientific and Technological Development Priority Level: д, е

Goals and objectives

Research directions: Modeling climatic processes

Project objective: Applying and enhancing methods of researching dynamics of complex systems for studying climatic systems of the Earth and related problems including forecasting extreme phenomena

The practical value of the study

  • A method has been created to extract main nonlinear modes of climatic variability from data. Analysis of ocean surface temperature series allows to pinpoint small dimension modes that are responsible for interannual and interdecade variability of climate.
  • A new method has been created to construct empirical models used on spatiotemporal decomposition of data and neural network representation of system state operator. As exemplified by interannual forecast of key climatic indexes it has been demonstrated that forecasting ability of models built using the new method can compete with best the bast existing analogs.

Implemented results of research:

We have developed algorithm used in modeling physical processes, in big data processing and machine learning. We have registered 11 useful software products with the state regulators:

  • «Software for calculating spatiotemporal mode on a multi-dimensional temporal series»
  • «Software for calculating nonlinear dynamic mode and its substantiation on multi-dimensional time series (version 1)»
  • «Software for calculating nonlinear dynamic mode and its substantiation on multi-dimensional temporal series with dimensional optimization»
  • «Software for calculating stochastic models of evolution operator based on artificial neural network, its substantiation and forecast behavior on a scalar time series»
  • «Software for computing numerical scheme of climatic models based on the method of Bayesian averaging on a regular grid latitude-longitude generation»
  • «Software for computing statistical momentums and cumulants of anomalies from a set interval of temporal scales from data on grid latitude-longitude generation»
  • «Software for computing forecasting model of evolution operator in the form of complex value artificial neural network»
  • «Software for computing empirical forecasting model of evolution operator with optimal nesting structure search on multi-dimensional time series»
  • «Software for computing Bayesian justification of evolution operator model in the form of complex-value artificial neural network»
  • «Software for computing empirical forecasting model of evolution operator on multi-dimensional time series accounting for its smoothness»
  • «Software for computing complex-value spatiotemporal mode on a multi-dimensional time series»

Education and career development:

  • We have organized the international «Conference on Mathematical Geophysics CMG 2018» (Russia, 2018)
  • We have organized sections at international conferences «EGU General Assembly» (Austria, 2015- 2018), «Topical problems of nonlinear wave physics» (Russia, 2014, 2017), «Frontiers of nonlinear physics» (Russia, 2016), and of «Nonlinear waves» international scientific schools (Russia, 2016, 2018.
  • We have organized the All-Russian School and Conference of Young Scientists «Composition of the atmosphere. Atmospheric electricity. Climate effects» (Russia, 2016).
  • We have organized work groups «Network analysis and data driven modeling of the climate» (Germany, 2014) and «Analysis of Dynamic Networks and Data Driven Modeling of the Climate» (Germany, 2015).
  • 3 of the Laboratory's employees have completed internships in empirical modeling of distributed systems.
  • The laboratory has participated in reading lecture courses «Mechanics», «Molecular physics», «Oscillations and waves theory», «General atmospheric circulation and its mathematical modeling», «Basics of climate theory», «Control theory», «Informational neurodynamic».
  • The laboratory has published a textbook

Organizational and structural changes:

The Laboratory now has a high performance computational cluster (over 500 computational course with 2.4 GHz frequency). This cluster is integrated into the infrastructure of the institute and is the core of the computational system of the Institute o Applied Physics of the Russian Academy of Sciences.


  • Potsdam Institute for Climate Impact Research (Germany): joint research and scientific events, developing new methods of detecting interactions and directions of links in complex systems, developing a common concept of basin stability of dynamic systems and its application to researching stability of climate modes detected in both regional climatic systems and in the global climatic system of the Earth
  • University of California Los Angeles (USA): joint research, creating empirical forecasting models for dynamics of Article marine ice and changes of ocean water level in the Arctic describing evolution at seasonal, interannual and decade scales

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Gavrilov A., Seleznev A., Mukhin D., Loskutov E., Feigin A., & Kurths J.
Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics 16 May: 1-18 (2018).
Mukhin D., Gavrilov A., Loskutov E., Feigin A., & Kurths, J.
Nonlinear reconstruction of global climate leading modes on decadal scales. Climate Dynamics September: 2301-2310 (2018).
Klinshov V., Kirillov S., Kurths J., Nekorkin V.
Interval stability for complex systems. New Journal of Physics 20 (2018).
Boers N., Chekroun M.D., Liu H., Kondrashov D., Rousseau D.D., Svensson A., … Ghil M.
Inverse stochastic–dynamic models for high-resolution Greenland ice core records. Earth System Dynamics 15 December: 1171–1190 (2017).
Volodin E. M., Mortikov E. V., Kostrykin S. V., Galin V. Y., Lykosov V. N., Gritsun A. S., … Yakovlev N. G.
Simulation of modern climate with the new version of the INM RAS climate model. Izvestiya, Atmospheric and Oceanic Physics 53(2): 142–155 (2017).
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