Bogumił Jakubiak

Abstract

Climate is a composite system consisting of five major interactive adjoint components: the atmosphere, the hydrosphere, the oceanosphere, the cryosphere, the lithosphere and the biosphere. In the paper the nature, state and variability of the climate system are described briefly. Of particular importance in open systems such as components of the climatic system is feedback. Feedback mechanisms act as internal controls of the system and result from a special adjustment among two or more subsystems. The meteorological, oceanic and glacial records show considerable variabil-ity on all time scales. Starting from chosen elements of the observed main state of the atmosphere (air temperature, atmospheric circulation, precipitation and evaporation) interannual and interdecadal variability in the climate system is briefly described. Such natural phenomena as quasibiennial oscillations (QBO) in the stratosphere, the El Niño — Southern Oscillation (ENSO) in the tropics and regional teleconnections such as the North Atlantic Oscillation are examples of such variability. The climate of the earth has undergone large changes in the past, is changing now and will change in the future. External factors (solar radiation, absorbing gases in the atmosphere, ice cover) and the thermodynamic quantities that characterize the climate (temperature, density, velocity, moisture content, salinity) are all interrelated through a set of physical laws expressed by various equations based on the general principles of conservation of mass, momen-tum and energy. The set of coupled partial differential equations can be solved subject to knowledge of the solar radiation input and other specified boundary and initial conditions that define the instantaneous state of a climate system. Mathematical models provide a new way to not only understand the climate's behaviour, but also to explore the possibility of future climate developments being predicted.

Keywords: climate, numerical climate models, climate variability

Bogumił Jakubiak, Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University Pawińskiego 5a, 02-106 Warsaw, Poland

Abstract

TO the study of climatic changes in the Europe-North Atlantic region some methods classical to the meteorological community, such as an empirical orthogonal function (EOF) decomposition and analogues searching were combined with methods used by the contemporary theory of dynamical systems. In this paper we try to resolve a question about the nature of changes in the climatic system, starting from an investigation as to whether features of self-similarity typical for strange attractors (SA) remain invariant or break. The essence of our approach is a reconstruction of a coarse-grained dimension of a strange attractor on the basis of time series of meteorological data. The best results were obtained by joint application of the EOF and Takens methods with a local approximation of a strange attractor trajectory by the fixed mass (analogue) method. The presented method has value not only as a research tool and has since October 1944 been implemented operationally at IMWM (Poland) to forecast a half-year air temperature anomaly within the mid-latitude belt.

Keywords: climate modelling, climate change, chaotic strange attractor, empirical long-term forecasting

Bogumił Jakubiak, Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University Pawińskiego 5a, 02-106 Warsaw, Poland
Dmitry Sonechkin, Hydrometeorological Research Centre of Russia Bolshoy Predtechensky Lane 9/13, 123242 Moscow, Russia
Nadjezda Ivachtchenko, Hydrometeorological Research Centre of Russia Bolshoy Predtechensky Lane 9/13, 123242 Moscow, Russia

Abstract

The authors discuss the average air temperature and mean precipitation amounts in long-term and short — time variability. This research may be a starting point for combining GCM's models (GFDL and GISS) used to forecast spatial distribution of climatic changes in hydrological models on the territory of Poland.Methods of grid simulation using 90-year and 30-year sequences provide sufficient statistical material for a relatively precise estimation. Authors presented stochastic generation of meteorological data in others time step (daily, monthly and yearly) by Markov chain.

Keywords: scenarios of global climate change; GCM models application (GFDL, GISS); generation of meteorological data (air temperature, precipitation); Markov chains application

Małgorzata Gutry-Korycka, Faculty of Geography and Regional Studies, University of Warsaw Krakowskie Przedmieście 30, 00-927 Warszawa, Poland
Bogumił Jakubiak, Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University Pawińskiego 5a, 02-106 Warsaw, Poland