Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431300
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data-driven approach to identifying key regions of change associated with future climate scenarios
1. A data-driven approach to identifying
key regions of change associated
with future climate scenarios
https://zacklabe.com/ @ZLabe
Zachary M. Labe
Postdoc in Seasonal-to-Decadal Variability and Predictability Division
NOAA GFDL and Princeton University
with…
Thomas L. Delworth, NOAA GFDL
Nathaniel C. Johnson, NOAA GFDL
William F. Cooke, NOAA GFDL
31 January 2024 – 104th
AMS Annual Meeting
Joint Session, J12A – AIES/CVC/Presidential
8. THE REAL WORLD
(Observations)
Let’s run a
climate model
One ensemble member
2022
1930 2050
Data
from
SPEAR_M
ED
What future climate scenario are we following?
9. THE REAL WORLD
(Observations)
Let’s run a
climate model
again!
Two ensemble members
Data
from
SPEAR_M
ED
What future climate scenario are we following?
10. THE REAL WORLD
(Observations)
Let’s run a
climate model
again & again!
Three ensemble members
Data
from
SPEAR_M
ED
What future climate scenario are we following?
12. What future climate scenario are we following?
THE REAL WORLD
(Observations)
CLIMATE MODEL
LARGE ENSEMBLE
NOAA GFDL – SPEAR_MED
Fully-Coupled (AM4/LM4/MOM6/SIS2)
Historical + SSP5-8.5
0.5° land/atmosphere, 1.0° ocean
also: LO, HI, HI_25 resolutions
https://www.gfdl.noaa.gov/spear/
30 ensemble
members in
GFDL SPEAR
28. What climate
patterns are
associated with
these transitions?
Transition from
SSP5-8.5 to SSP2-4.5
Transition from
SSP5-8.5 to SSP2-4.5
Transition from
SSP2-4.5 to SSP1-1.9
2031 Rapid mitigation
2040 Rapid mitigation
30. XAI composites of years associated with the transition from SSP5-8.5 to SSP2-4.5
(a) approx. 2055-2060 (b) approx. 2040-2045
31. Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the
regional emergence of climate patterns in the ARISE-SAI-1.5
simulations. Environmental Research Letters, DOI:10.1088/1748-
9326/acc81a
Framework can be
applied to different
geographic regions
and climate variables
Parallel approach for
detecting climate
intervention scenarios
32. Framework can be
applied to different
geographic regions
and climate variables
Parallel approach for
detecting climate
intervention scenarios
Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the
regional emergence of climate patterns in the ARISE-SAI-1.5
simulations. Environmental Research Letters, DOI:10.1088/1748-
9326/acc81a
33. KEY POINTS
1. A neural network applied to large ensembles can distinguish annual mean
maps of climate variables for a range of different climate scenarios
2. Regional patterns are revealed by explainable AI are critical for
distinguishing climate scenarios even under similar global mean warming
3. Emission scenario classification for the second half of the 21st century is
sensitive to a difference in timing of mitigation by ten years
zachary.labe@noaa.gov
Wednesday, 31 January 2024
104th American Meteorological Society Annual Meeting
Joint Session, J12A – AIES/CVC/Presidential