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BEGIN:VEVENT
CATEGORIES:Lectures and Seminars,SMAST,SMAST Seminar Series
DESCRIPTION:Seminar Announcement Department of Estuarine and Ocean Sciences
    "Using machine Learning to Improve Simulation of Phytoplankton Size Cl
 asses in Earth System Models"   Sandupal Dutta PhD Candidate, Dept. Earth
  and Planetary Sciences, Johns Hopkins University   Wednesday, November 4
 , 2026 12:30 - 1:30 pm SMAST E 101-103 and via Zoom   Abstract: Marine ph
 ytoplankton is responsible for fixing approximately 50 Gt C/yr and powers 
 the biological pump, which is an important part of the carbon cycle. Phyto
 plankton are often classified according to their sizes. Earth System model
 s (ESMs) such as the E3SM, NCAR CESM2, NASA GISS has significant biases in
  predictions of plankton size classes. We use satellite ocean color data f
 rom Copernicus and Scientific Machine Learning to alter the governing equa
 tion of plankton used by MARBL (Marine Biogeochemistry Library), biogeoche
 mistry module of the ESMs, to improve the prediction of plankton size clas
 ses by ESMs.   Join Meeting https://umassd.zoom.us/j/97440069270 Note: Me
 eting ID and passcode required. Email contact to obtain.   For additional
  information, please contact Callie Rumbut at c.rumbut@umassd.edu    \nE
 vent page: https://www.umassd.edu/events/cms/11-4-26-using-machine-learnin
 g-to-improve-simulation.php\nEvent link: https://umassd.zoom.us/j/97440069
 270
X-ALT-DESC;FMTTYPE=text/html:<html><body><p style="text-align: center\;"><s
 trong>Seminar Announcement</strong></p>\n<p style="text-align: center\;"><
 strong>Department of Estuarine and Ocean Sciences</strong></p>\n<p style="
 text-align: center\;"><strong> </strong></p>\n<p style="text-align: cente
 r\;"><span style="font-size: 14.0pt\; line-height: 115%\;">"Using machine 
 Learning to Improve Simulation of Phytoplankton Size Classes in Earth Syst
 em Models"</span></p>\n<p style="text-align: center\;"><span style="font-s
 ize: 14.0pt\; line-height: 115%\;"> </span></p>\n<p style="text-align: ce
 nter\;"><span style="font-size: 14.0pt\; line-height: 115%\;">Sandupal Dut
 ta</span></p>\n<p style="text-align: center\;"><span style="font-size: 14.
 0pt\; line-height: 115%\;">PhD Candidate\, Dept. Earth and Planetary Scien
 ces\, Johns Hopkins University</span></p>\n<p> </p>\n<p style="text-align
 : center\;">Wednesday\, November 4\, 2026</p>\n<p style="text-align: cente
 r\;">12:30 - 1:30 pm</p>\n<p style="text-align: center\;">SMAST E 101-103 
 and via Zoom</p>\n<p> </p>\n<p><strong>Abstract:</strong></p>\n<p>Marine 
 phytoplankton is responsible for fixing approximately 50 Gt C/yr and power
 s the biological pump\, which is an important part of the carbon cycle. Ph
 ytoplankton are often classified according to their sizes. Earth System mo
 dels (ESMs) such as the E3SM\, NCAR CESM2\, NASA GISS has significant bias
 es in predictions of plankton size classes. We use satellite ocean color d
 ata from Copernicus and Scientific Machine Learning to alter the governing
  equation of plankton used by MARBL (Marine Biogeochemistry Library)\, bio
 geochemistry module of the ESMs\, to improve the prediction of plankton si
 ze classes by ESMs.</p>\n<p> </p>\n<p><strong>Join Meeting</strong></p>\n
 <p>https://umassd.zoom.us/j/97440069270</p>\n<p>Note: Meeting ID and passc
 ode required. Email contact to obtain.</p>\n<p> </p>\n<p><em>For addition
 al information\, please contact Callie Rumbut at c.rumbut@umassd.edu</em><
 /p>\n<p> </p>\n<p> </p><p>Event page: <a href="https://www.umassd.edu/ev
 ents/cms/11-4-26-using-machine-learning-to-improve-simulation.php">https:/
 /www.umassd.edu/events/cms/11-4-26-using-machine-learning-to-improve-simul
 ation.php</a><br>Event link: <a href="https://umassd.zoom.us/j/97440069270
 ">https://umassd.zoom.us/j/97440069270</a></p></body></html>
DTSTAMP:20261027T225710
DTSTART;TZID=America/New_York:20261104T123000
DTEND;TZID=America/New_York:20261104T133000
LOCATION:SMAST East 101-103
SUMMARY;LANGUAGE=en-us:Using machine Learning to Improve Simulation of Phyt
 oplankton Size Classes in Earth System Models
UID:cb0557d1cbe9d80ec666c404a60b49f6@www.umassd.edu
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