Pan-Eurasian EXperiment (PEEX) Modelling Platform research and development for online coupled integrated meteorology-chemistry-aerosols feedbacks and interactions in weather, climate and atmospheric composition multi-scale modelling

The PEEX-MP (https://www.atm.helsinki.fi/peex/index.php/modelling-platform) presents a strategy for best use of current generation modeling tools to improve process understanding and improve predictability on different timescales within the PEEX domain, and also presents potential future developments. A number of application areas of new integrated modelling developments are expected to be considered, including: (i) improved numerical weather prediction and chemical weather forecasting with short-term feedbacks of aerosols and chemistry on meteorological variables; (ii) two-way interactions between atmospheric pollution/ composition and climate variability/ change; (iii) better prediction of atmosphere and/or ocean state through closer coupling between the component models to represent the two-way feedbacks and exchange of the atmospheric and ocean boundary layer properties; (iv) more complete/ detailed simulation of the hydrological cycle, through linking atmospheric, land surface, ecosystems, hydrological and ocean circulation models.
The PEEX-MP focuses on new generation of integrated models and is based on the seamless Earth System Modelling approach to evolve from separate model components to seamless meteorology-composition-environment models to address challenges in weather, climate and atmospheric composition fields whose interests, applications and challenges are now overlapping. Several models, being a part of the PEEX-MP, are to be further developed and tested in this HPC project.
The overall objectives will be to analyse the importance of the meteorology-chemistry-aerosols interactions and feedbacks and to provide a way for development of efficient techniques for on-line coupling of numerical weather prediction and atmospheric chemical transport via process-oriented parameterizations and feedback algorithms, which will improve the numerical weather prediction, climate and atmospheric composition forecasting.