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Atmospheric component of the climate system



Scientific coordinator

Elena Roget correu

Web contents manager

Anna Drou correu

Atmospheric component of the climate system

Work package number


The Atmosphere Component of the Climate System



1) Knowledge of the background atmospheric circulation changes which underlie observed variations in the inland seas.

2) Identification of extreme weather events considering that scientific studies have demonstrated that a changing climate is accompanied by changes in the intensity, duration, frequency and spatial distribution of extreme events.



Description of work

Task 6.1:  Predict future changes under different climate change scenarios relevant for each of the 4 seas

6.1.1) Statistical downscaling. We’ll analyse several ensembles of coupled model experiments (scenario runs) from IPCC AR4 models and statistically project the leading modes of European climate variability as well as cyclone activity parameters onto regional climate characteristics. Besides the routine metrics of cyclone activity, the transport of water vapour by atmospheric cyclones as well as regional atmospheric moisture recycling parameters will be considered. In particular we are going to consider the responses of precipitation totals and distribution parameters on the moisture transport and cyclone activity. Projections will be performed using known linear and non-linear statistical techniques, orthogonal decompositions and correlation analysis. This will allow for identification of the climate variability modes primarily responsible for the local climate change and identification and quantification of sensitivity. Since regional parameters of climate variability are to be considered first, we will analyse local characteristics of precipitation, temperature, sea level, soil moisture and marine climate parameters (wherever available). We will also analyse regional moisture budgets and moisture recycling characteristics in response to continental-scale climate variability and change.

6.1.2) Dynamical downscaling. Large potential also lies in performing numerical experiments with state–of-the-art regional GCMS forced by surface boundary conditions from IPCC AR4 coupled model simulations under different scenarios. When compared with similar runs for the present climate and with those with observed conditions (e.g. from reanalyses), these will discriminate the effects of natural variability and anthropogenic climate change and provide a well-justified attribution of the observed changes.

6.1.3) Quantify the response of regional sea level changes onto climate forcing. We will make use of the results of coupled model experiments for the 21st century to provide time series of the regional precipitation under present and future climate conditions in order to estimate sea level variations. Arpe and Leroy (2007) have already used the ECHAM5-OM model simulations with increased greenhouse gas concentration to investigate the future level of the Caspian Sea.

Task 6.2: Spatial and temporal variability of local atmospheric circulation pattern

6.2.1) To objectively define the leading modes of circulation variability for the period of the order of 100 years (principal component analysis – PCA – of the monthly and seasonal mean SLP and Z500 fields for the Atlantic-Western Eurasia sector of the Northern Hemisphere).

6.2.2) To perform trend and decadal variability analysis of the leading principal components and the NH circulation indices.

6.2.3) To perform analysis of the co-variability between the circulation indices and inland sea characteristics.

6.2.4) To perform an analysis of the temporal changes in leading modes of circulation variability.

Task 6.3: Linking of local extremes to large-scale atmospheric circulation structures

6.3.1) Identification of extreme events such as heat waves, heavy precipitation, low temperature, storms, floods and droughts in the study regions using historical observational data. It could be done from a comprehensive dataset developed in WP3. Intensity, duration, frequency and spatial distribution of extreme events as well as trend changes of these characteristics will be revealed using statistical approaches such as frequency distribution analysis, composite analysis, cluster analysis, trend analysis, spectral analysis and wavelet analysis. 

6.3.2) Identification of weather regimes with which extreme events are associated. This part of the work will be closely interrelated with the next task where the leading modes of circulation variability for the period of the order of 100 years on the basis of principal component analysis of the SLP and Z500 fields for the Atlantic-Western Eurasia sector of the Northern Hemisphere will be objectively identified. Optimization procedures to establish the connection between leading modes of SLP and Z500 anomalies and local extremes will be performed.

6.3.3) Analysis of blocking anticyclones versus extreme events in the studied regions. On the basis of created in RIHMI-WDC and RHMC catalogue from synoptic surface and 500mb level charts about blocking conditions in Euro-Atlantic region it is possible to investigate the relationship between occurrences of extreme weather and characteristics of blocking anticyclones. Preliminary results for the influence of blocking anticyclone conditions in western Europe on extreme snowfall in the north-European part of Russia are discussed in Khan et al. (2009).

6.3.4) Verification of the ability of numerical models to simulate extreme events. Outputs of numerical model experiments from other tasks will be processed to identify anomalous conditions. Extreme events revealed from modelled data will be validated against extreme events from historical observational data. Tendencies in the occurrences and intensity of extreme events under a double CO2 scenario will be done using forecasts from numerical experiments.



D6.1 Estimation of climate trends.

D6.2 Complete overview of circulation classification patterns.

D6.2 Estimation of regional extreme indices.

D6.3 Report about frequency changes in extreme events and their tendencies.

D6.4 Highest-resolution output models using the best model methods available today.

D6.5 Assess the quality of older models and modify predictions about future climate change in the study region.


Researchers involved

T6.1: RHMC: K. Rubinstein, R. Ignatov, E. Nabokova; SIO: S. Gulev, K. Korotenko, O. Zolina, E. Rudeva, A. Gavrikov. UdG: J. Calbó; J. Badosa

T.6.2: SIO: P. Zavialov, A. Kazmin; UdG: J. Calbó; UBRUN: K. Arpe.

T6.3: RHMC: V. Khan, V. Tischenko; V.Kryjov, M. Shatunova; ULIV: A. Morse