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X-WR-CALNAME:Transferring Climate Change Physical Knowledge
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UID:2025-02-03-francesco-immorlano@cml.ics.uci.edu
DTSTAMP:20250203T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250203T130000
DTEND;TZID=America/Los_Angeles:20250203T140000
SUMMARY:[CML Seminar] Francesco Immorlano: Transferring Climate Change Phys
ical Knowledge
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Francesco Immorlano\, Postdoctoral Researcher\, Department of C
omputer Science\, University of California\, Irvine\n\nTitle: Transferring
Climate Change Physical Knowledge\n\nAbstract: Earth system models (ESMs)
are the main tools currently used to project global mean temperature rise
according to several future greenhouse gases emissions scenarios. Accurat
e and precise climate projections are required for climate adaptation and
mitigation\, but these models still exhibit great uncertainties that are a
major roadblock for policy makers. Several approaches have been developed
to reduce the spread of climate projections\, yet those methods cannot ca
pture the non-linear complexity inherent in the climate system. Using a Tr
ansfer Learning approach\, Machine Learning can leverage and combine the k
nowledge gained from ESMs simulations and historical observations to more
accurately project global surface air temperature fields in the 21st centu
ry. This helps enhance the representation of future projections and their
associated spatial patterns which are critical to climate sensitivity.\n\n
https://cml.ics.uci.edu/seminars/2025-02-03-francesco-immorlano
X-ALT-DESC;FMTTYPE=text/html:Francesco Immorlano\, Postd
octoral Researcher\, Department of Computer Science\, University of Califo
rnia\, Irvine
Title: Transferring Climate Change Physical Kn
owledge
Abstract: Earth system models (ESMs) are the main to
ols currently used to project global mean temperature rise according to se
veral future greenhouse gases emissions scenarios. Accurate and precise cl
imate projections are required for climate adaptation and mitigation\, but
these models still exhibit great uncertainties that are a major roadblock
for policy makers. Several approaches have been developed to reduce the s
pread of climate projections\, yet those methods cannot capture the non-li
near complexity inherent in the climate system. Using a Transfer Learning
approach\, Machine Learning can leverage and combine the knowledge gained
from ESMs simulations and historical observations to more accurately proje
ct global surface air temperature fields in the 21st century. This helps e
nhance the representation of future projections and their associated spati
al patterns which are critical to climate sensitivity.
https://cml.
ics.uci.edu/seminars/2025-02-03-francesco-immorlano
URL:https://cml.ics.uci.edu/seminars/2025-02-03-francesco-immorlano
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