Applied methodologies for improved exchange between atmospheric e-science Infrastructures at high latitudes
The focus of the project is on the application of of eScience tools, but applied to climate and air quality research at high northern latitudes. We will generate three two-week graduate courses, open to Nordic graduate students and early career scientists. The courses will be designed and launched in a collaboration between Stockholm University, University of Helsinki, Norwegian Meteorological Institute and IT center for Science (CSC).
It is well appreciated within the scientific community that we are facing a global warming that is caused by excessive anthropogenic emission of greenhouse gases. These gases perturb the radiative budget of the atmosphere by trapping a fraction of outgoing infrared radiation before it is lost to space. Increased temperature has numerous effects on the climate system. However, not only greenhouse gases are emitted from anthropogenic activities, but also aerosols and aerosol precursors. In the climate system, these aerosols can in turn perturb the radiative budget by scattering and absorbing incoming short wave radiation, either directly or indirectly through modulation of cloud radiative properties and lifetime. The net effect of the aerosols is believed to substantially counteract the warming effect of greenhouse gases.
However, describing the aerosol effect in the climate system is inherently difficult; the lifetime is comparably short, causing a high temporal and spatial variability and the radiative effects is hard to assess, both quantitatively and qualitatively.
In order to resolve the net climate effect more and more effort is put into design and application of climate models, increasing the level of details in the process descriptions. Also, international and national initiatives has produced, and is producing an ever increasing amount of atmospheric data. All in all, the amount of data, often measured in terra-bytes, call for non-traditional evaluation methods in order to benefit from the full complexity of both model output and observations.