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STREAM (Stratospheric and Tropospheric REsearch And Modeling)

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Scientic Supervisor / Contact Person

Name and Surname
Carlos Ordóñez

Localization & Research Area

Faculty / Institute
Faculty of Physical Science
Earth Physics and Astrophysics
Research Area
Environmental Sciences and Geology (ENV)

MSCA & ERC experience

Research group / research team hosted any MSCA fellow?
Research group / research team have any ERC beneficiaries?

Research Team & Research Topic

Research Team / Research Group Name (if any)
STREAM (Stratospheric and Tropospheric REsearch And Modeling)
Website of the Research team / Research Group / Department
Brief description of the Research Team / Research Group / Department
The Stratospheric and Tropospheric REsearch And Modeling (STREAM) group is a research team at Universidad Complutense de Madrid. We are devoted to the analysis and modelling of the atmospheric circulation and climate variability. The group has more than ten members, including permanent staff, post-doctoral researches and PhD students. We have more than 10 years of experience in the field, and have established a dense network of national and international collaborations. The research activity of STREAM is organized around six main topics: tropospheric climate variability, extreme events, atmospheric composition and dynamics, machine learning methods, climate reconstruction in the last 500 years and stratospheric dynamics.

With regard to the proposed research line, the group has expertise in developing catalogues of synoptic weather patterns and evaluating their impact on the concentrations of air pollutants. For instance, we have found distinct responses of European ozone and particulate matter (PM) to anticyclonic systems and air stagnation, we have used clustering techniques to determine the meteorological drivers of ozone extremes and we have assessed the role of the extratropical jet latitude on the concentrations of PM in Europe ( We also have experience in analysing climate and weather extremes through the use of models, observations and statistical techniques (
Research lines / projects proposed
Observational studies have shown strong relationships between the concentrations of air pollutants such as ground-level ozone and particulate matter (PM) with meteorological parameters. Understanding such relationships is often complicated by the covariance of the meteorological variables as well as by the effect of emission changes.

Climate change is expected to increase the risk of extreme air pollution events in the future. As an example, a warming climate can increase peak levels of ozone in polluted regions through a number of processes, an impact that has been referred to as "climate penalty". Moreover, climate models have projected increases in atmospheric stagnation, which will make it harder to achieve air quality goals for most pollutants.

We are looking for candidates with an interest in using observations, chemical transport models, chemistry-climate models, statistical techniques and/or machine-learning methods, with the aim of improving our understanding of the interactions between the atmospheric circulation, local meteorology and emissions to shape the variability of air pollutants on different time scales.

We would like to address questions like: Are numerical models able to reproduce the observed impacts of atmospheric circulation on air quality" Will changes in the frequency and duration of atmospheric blocking and air stagnation, or in the latitudinal position of the extratropical jet, influence future air quality" How do changes in the circulation interact with the local meteorology to trigger the occurrence of air pollution extremes" To what extent can abatement strategies (e.g. changes in the vehicle fleet) offset the effect of climate change on air quality" Applications on these or similar topics are welcome.

Application requirements

Professional Experience & Documents
- Curriculum vitae
- Letter of motivation
- Two recommendation letters
You can attach the 'One Page Proposal' to enhance the attractiveness of your application. Supervisors usually appreciate it. Please take into account your background and the information provided in Research Team & Research Topic section to fill in it.

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