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<div class="ContentPasted0">COAPS Short Seminar Series </div>
<div class="ContentPasted0">11:00 AM April 1st</div>
<div class="ContentPasted0">Attend F2F (in 255 Research A) or Virtually (via Zoom)</div>
<div class="ContentPasted0">https://fsu.zoom.us/j/92268262553</div>
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<div class="ContentPasted0">Meeting ID: 922 6826 2553</div>
<div class="ContentPasted0">Talks are 12 minutes long with an additional 8 minutes for questions.
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<div class="ContentPasted0">Observed Variability of Upwelling Filaments in the Northern Arabian Sea during Summer Monsoons</div>
<div class="ContentPasted0">By Ethan Wright</div>
<div class="ContentPasted0">Cold filaments of upwelled water form off the coasts of Oman and Yemen during the Indian summer monsoon and strongly contribute to cross-shelf exchanges of water mass properties and nutrients. The largest of the cold filaments typically
form around the northern periphery of persistent anticyclonic eddies located off the coasts during the summer upwelling season. A novel filament detection algorithm with satellite sea surface temperature imagery is used to analyze the seasonal formation and
interannual variability of upwelling filaments along the coasts of Arabia during summer monsoons. In particular, mesoscale eddy patterns associated with the development large cold filaments are analyzed using a combination of satellite altimetry and sea surface
temperature products, providing insight into the role of eddies in cross-shelf exchanges of water in the Northern Arabian Sea.</div>
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<div class="ContentPasted0">The changing annual cycle of SST</div>
<div class="ContentPasted0">By Fucheng Yang</div>
<div class="ContentPasted0">Description: In recent decades, many research efforts focused on global climate change, multidecadal, decadal, interannual variability, and the increasing extreme events of sea surface temperature. In contrast, the continuous evolution
of the reference frame, the annual cycle of SST used to quantify the aforementioned variability and changes, has long been overlooked, resulting in difficulties in understanding the underlying physical mechanisms responsible for these variability and changes.
In this study, we strive to bridge this gap on the phase changes in SST annual cycle. By devising a running correlation-based method, we can now quantify the non-sinusoidal shape of the evolving SST annual cycle, such as the advancing or delaying of summer
and winter peaking times. It is revealed that the varying phases of summer or winter are more closely linked to multidecadal SST variability than to long-term climate change. Both the systematic shift of the phase and alterations in the annual cycle shape
contribute to the phase changes, which explain 0.4~1.0 °C of monthly SST anomaly with respect to the climatological annual cycle in a multidecadal timescale. Furthermore, it is evident that the SST phases in historical simulations are not well captured and
exhibit stronger variation compared with observation.</div>
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<div class="ContentPasted0">Introduction to Dynamic Mode Decomposition (DMD)</div>
<div class="ContentPasted0">By Takaya Uchida</div>
<div class="ContentPasted0">DMD is a method often applied to identify spatial modes that may be hidden in the dataset. There are similarities to Empirical Orthogonal Functions (EOFs). I will a brief overview of DMDs and provide a in-person demonstration on
the utility of DMDs applied to toy datasets and observational data.\</div>
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<div class="ContentPasted0">NOTE: Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.</div>
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