Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Buchkapitel

Visual analysis of spatio-temporal trends in time-dependent ensemble data sets on the example of the North Atlantic Oscillation

MPG-Autoren
/persons/resource/persons220984

Maher,  Nicola       
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37193

Jungclaus,  Johann H.       
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Vietinghoff, D., Heine, C., Böttinger, M., Maher, N., Jungclaus, J. H., & Scheuermann, G. (2021). Visual analysis of spatio-temporal trends in time-dependent ensemble data sets on the example of the North Atlantic Oscillation. In IEEE Pacific Visualization Symposium (pp. 71-80).


Zitierlink: https://hdl.handle.net/21.11116/0000-0008-B892-3
Zusammenfassung
A driving factor of the winter weather in Western Europe is the North Atlantic Oscillation (NAO), manifested by fluctuations in the difference of sea level pressure between the Icelandic Low and the Azores High. Different methods have been developed that describe the strength of this oscillation, but they rely on certain assumptions, e.g., fixed positions of these two pressure systems. It is possible that climate change affects the mean location of both the Low and the High and thus the validity of these descriptive methods. This study is the first to visually analyze large ensemble climate change simulations (the MPI Grand Ensemble) to robustly assess shifts of the drivers of the NAO phenomenon using the uncertain northern hemispheric surface pressure fields. For this, we use a sliding window approach and compute empirical orthogonal functions (EOFs) for each window and ensemble member, then compare the uncertainty of local extrema in the results as well as their temporal evolution across different CO2 scenarios. We find systematic northeastward shifts in the location of the pressure systems that correlate with the simulated warming. Applying visualization techniques for this analysis was not straightforward; we reflect and give some lessons learned for the field of visualization. © 2021 IEEE.