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Data Analysis and Model Evaluation Tools in Environmental and Climate Science

The course in 2022 will introduce Earth system analysis as well as data analysis with practical exercises.

The course will make use of existing infrastructures, such as climate models (e.g., NorESM, EC-EARTH), model databases (e.g., AeroCom, CMIP6), model data evaluation portals (e.g., AeroCom), and atmospheric and oceanic databases (e.g., EBAS, ORA-IP). Practical work is initiated and accompanied to apply modern visualization, data analysis and statistical tools (e.g., Jupyter notebooks, AeroCom tools).

Introductions will be given on the role of aerosols and clouds, observational techniques,  Earth system models, climate forcing and climate model evaluation. The course involves a set of relevant lectures and tutorials, with the main emphasis placed on intensive group work and a final report that will be written during and after the course by each student. Before the course, the selected students will be asked to practice the tools to be used on the course (mainly Python and Jupyter notebook) by solving a pre-exercise.

The course is primarily aimed at PhD students in atmospheric and biospheric sciences (also advanced MSc students are welcome to apply). During the course the students can either use their own data or utilize provided model data together with long-term aerosol, air, ion, trace gas, meteorological data measured at field stations. Topics for practical work will be suggested depending on student’s background. This year, the topics will relate to the core themes identified in the CRiceS project. Related introductory lectures will be given by researchers from the CRiceS consortia.

  • Course structure

    Detailed course content

    Introductory lectures on:

    • Lectures on core themes of CRiceS
    • Climate model evaluation& Climate model diagnostics
    • Observational methods (in-situ and remote sensing techniques)
    • Model analysis tool introductions
    • Model data base structures
    • Tips & Tricks with Python and Jupyter notebooks / Jupyter Hub

    Practical work

    Students are asked to cooperate in small groups (3-4) with an assistant on individual topics of interest in the realm of climate model evaluation and analysis. Jupyter notebooks shall be compiled to document the work and results. Two presentations are expected during the course to report on progress.


    The learning outcomes

    In the end of the course the student will have

    • skills to analyze a scientific problem within the Earth system;
    • skills to set up small python based data analysis projects;
    • knowledge about existing online databases containing atmospheric and ecosystem data;
    • the ability to understand, evaluate and visualize model output;

    Some of the transferable skills the course strives to improve

    • statistical analysis of model and field measurements;
    • multidisciplinary approach;
    • project management; and
    • collaborative learning.



    Your rights and responsibilities

    Your rights and responsibilities as a student

  • Contact

    Study counsellors

    Questions about admission, registration, schedule etc.

    Course coordinator

    Paul Zieger,