TY - GEN
T1 - Visual Analysis of Spatio-Temporal Trends in Time-Dependent Ensemble Data Sets on the Example of the North Atlantic Oscillation
AU - Vietinghoff, Dominik
AU - Heine, Christian
AU - Bottinger, Michael
AU - Maher, Nicola
AU - Jungclaus, Johann
AU - Scheuermann, Gerik
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Computer vision
KW - Computer vision problemsObject detection
KW - Human-centered computing
KW - Visualization
KW - Visualization systems and tools
KW - Visualization toolkits; Computing methodologies
UR - http://www.scopus.com/inward/record.url?scp=85107395518&partnerID=8YFLogxK
U2 - 10.1109/PacificVis52677.2021.00017
DO - 10.1109/PacificVis52677.2021.00017
M3 - Conference contribution
T3 - IEEE Pacific Visualization Symposium
SP - 71
EP - 80
BT - Proceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021
PB - IEEE Computer Society
T2 - 14th IEEE Pacific Visualization Symposium, PacificVis 2021
Y2 - 19 April 2021 through 22 April 2021
ER -