Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024). Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html References... Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)

1. EXPLAINABLE AI FOR DISTINGUISHING
FUTURE CLIMATE CHANGE
SCENARIOS
@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
16 April 2024 – EGU General Assembly (9110)
Machine Learning for Climate Science
Session ITS1.1/CL0.1.17
https://zacklabe.com/

2. 1) Where do we
go from here?
1
1850 1940 1980 2020

3. 1) Where do we
go from here?
2) How do we
disentangle
internal climate
variability?
Feb/Mar 2016
Sep 2023
2
1850 2020

4. 3) How do we
account for
regional
patterns of
change?
3
Warming
Cooling
Warming
Cooling
Temperature Trend (°C/decade)
Warming
Cooling
Warming
Cooling

5. Explainable neural networks can distinguish
different regional climate patterns driven by
time-evolving radiative forcing
TAKEAWAY MESSAGE
4

6. What future climate scenario are we following?
5

7. What future climate scenario are we following?
6
Radiative
Forcing
Scenarios

8. THE REAL WORLD
(Observations)
Data from
Berkeley Earth Surface Temperature
1930 2022
What future climate scenario are we following?
7

9. THE REAL WORLD
(Observations)
CLIMATE MODEL
LARGE ENSEMBLE
What future climate scenario are we following?
8

10. 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
9

11. Ensemble
members in
GFDL SPEAR
Maps of a given period in each ensemble
Inputs for machine learning
10

12. Train a neural
network to predict
5 classes
(climate scenarios)
11

13. Yearly Maps of T2M
Yearly Maps of T2M
Neural
Network
Classify
Climate
Scenario
Artificial
Neural
Network
Output
=
5
Classes
Yearly Maps of T2M
Neural
Network
Binary Output Binary Output
Step #1
Read in gridded maps of a
climate variable from
SPEAR simulations
12

14. Yearly Maps of T2M
Yearly Maps of T2M
Neural
Network
Classify
Climate
Scenario
Artificial
Neural
Network
Output
=
5
Classes
Yearly Maps of T2M
Neural
Network
Binary Output Binary Output
Step #2
Feed data into an
artificial neural network
with three hidden layers
13

15. Yearly Maps of T2M
Yearly Maps of T2M
Neural
Network
Classify
Climate
Scenario
Artificial
Neural
Network
Output
=
5
Classes
Yearly Maps of T2M
Neural
Network
Binary Output Binary Output
Step #3
Classify which climate
scenario (n=5) is
associated with each map
14

16. Yearly Maps of T2M
Yearly Maps of T2M
Neural
Network
Classify
Climate
Scenario
Artificial
Neural
Network
Output
=
5
Classes
Yearly Maps of T2M
Neural
Network
Binary Output Binary Output
Step #4
“How” à XAI
Can a neural network learn unique patterns of climate
change related to each future emission scenario?
15

17. Predictions for
SPEAR_MED
Testing Data
Accuracy=92%
Nearer to predicted class
Further from predicted class
Composites of
XAI heatmaps
16

18. Global Mean
Surface Temperature
Can we identify changes in
future climate impacts after
rapid mitigation?
Two “overshoot”
scenarios conducted
with SPEAR_MED
17

19. 30 ensembles for GFDL
SPEAR_MED
30 ensembles for GFDL
SPEAR_MED
Input maps from
out-of-sample
ensembles into
classification
network
2020 2030 2100 18

20. 2031 2040
Rapid Mitigation
Rapid Mitigation
19

21. Classifications in
each year for the
ensemble mean from
SSP5-3.4OS
SSP5-8.5
SSP2-4.5
2015 2055 2065 2095
Neural
Network
“Confidence”
(fraction)
20

22. SSP5-8.5
SSP2-4.5
2055-2060
rapid mitigation begins
2015 2095
Classifications in
each year for the
ensemble mean from
SSP5-3.4OS
Neural
Network
“Confidence”
(fraction)
21

23. Are these predictions
robust across
ensemble members?
SSP5-3.4OS
Transition from
SSP5-8.5 to SSP2-4.5
2015 2060 2100
THAT SELECTED EACH CLIMATE SCENARIO CLASS
(n=30)
22

24. 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
23

25. XAI composites of years associated with the transition from SSP5-8.5 to SSP2-4.5
(a) approx. 2055-2060 (b) approx. 2040-2045
Nearer to SSP2-4.5
Nearer to SSP5-8.5
24

26. 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
25

27. 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 26

28. KEY POINTS
1. A neural network applied to large ensembles can link annual mean maps
of climate variables to a range of radiative forcing scenarios
2. Information extracted from regional change patterns is used to distinguish
between climate scenarios, even those with similar global warming
3. Radiative forcing scenario classifications for the later 21st century are
sensitive to a difference in the timing of mitigation by ten years
zachary.labe@noaa.gov
Tuesday, 16 April 2024 – EGU General Assembly
Machine Learning for Climate Science (#9110)
Session: ITS1.1/CL0.1.17
Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024).
Exploring a data-driven approach to identify regions of change
associated with future climate scenarios. (submitted)
Preprint (ESSOAr): https://doi.org/10.22541/essoar.171288901.17027965/v1
27