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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/
1) Where do we
go from here?
1
1850 1940 1980 2020
1) Where do we
go from here?
2) How do we
disentangle
internal climate
variability?
Feb/Mar 2016
Sep 2023
2
1850 2020
3) How do we
account for
regional
patterns of
change?
3
Warming
Cooling
Warming
Cooling
Temperature Trend (°C/decade)
Warming
Cooling
Warming
Cooling
Explainable neural networks can distinguish
different regional climate patterns driven by
time-evolving radiative forcing
TAKEAWAY MESSAGE
4
What future climate scenario are we following?
5
What future climate scenario are we following?
6
Radiative
Forcing
Scenarios
THE REAL WORLD
(Observations)
Data from
Berkeley Earth Surface Temperature
1930 2022
What future climate scenario are we following?
7
THE REAL WORLD
(Observations)
CLIMATE MODEL
LARGE ENSEMBLE
What future climate scenario are we following?
8
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
Ensemble
members in
GFDL SPEAR
Maps of a given period in each ensemble
Inputs for machine learning
10
Train a neural
network to predict
5 classes
(climate scenarios)
11
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
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
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
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
Predictions for
SPEAR_MED
Testing Data
Accuracy=92%
Nearer to predicted class
Further from predicted class
Composites of
XAI heatmaps
16
Global Mean
Surface Temperature
Can we identify changes in
future climate impacts after
rapid mitigation?
Two “overshoot”
scenarios conducted
with SPEAR_MED
17
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
2031 2040
Rapid Mitigation
Rapid Mitigation
19
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
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
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
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
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
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
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
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

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Explainable AI for distinguishing future climate change scenarios

  • 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
  • 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