1. An artificial neural network was developed to predict slowdowns in the rate of decadal global warming by analyzing patterns in ocean heat content anomalies. 2. The neural network found that transitions in the phase of the Interdecadal Pacific Oscillation often preceded warming slowdowns in climate model simulations. 3. Using explainable AI techniques, researchers determined the neural network was leveraging tropical patterns of ocean heat content to make its predictions of when decadal warming rates might slowdown.

1. DECADAL WARMING
SLOWDOWN PREDICTIONS
BY AN ARTIFICIAL NEURAL NETWORK
@ZLabe
Zachary M. Labe
with Elizabeth A. Barnes
Colorado State University
Department of Atmospheric Science
29 October 2021
Young Scientist Symposium on Atmospheric Research (YSSAR)

2. Global Warming
Hiatus?
…in research

3. Global Warming
Hiatus?
…in research

4. Global Warming
Hiatus?
…in research

5. Global Warming
Hiatus?
…in research

6. Global Warming
Hiatus?
…in research

7. Global Warming
Hiatus?
…in research

8. Global Warming
Hiatus?
…in the media, etc.

9. Global Warming
Hiatus?
>300 papers, to-date

10. Global Warming
Hiatus?

11. Are slowdowns (“hiatus”) in decadal
warming predictable?
• Statistical construct?
• Lack of surface temperature observations in the Arctic?
• Phase transition of the Interdecadal Pacific Oscillation (IPO)?
• Influence of volcanoes and other aerosol forcing?
• Weaker solar forcing?
• Lower equilibrium climate sensitivity (ECS)?
• Other combinations of internal variability?
FUTURE
WARMING

12. Select one ensemble
member and calculate
the annual mean
global mean surface
temperature (GMST)
2-m TEMPERATURE
ANOMALY

13. Calculate 10-year
moving (linear) trends
2-m TEMPERATURE
ANOMALY

14. Plot the slope of the
linear trends
START OF 10-YEAR
TEMPERATURE TREND
2-m TEMPERATURE
ANOMALY

15. Calculate a threshold
for defining a slowdown
in decadal warming

16. Repeat this exercise for
each ensemble
member in CESM2-LE

17. Compare warming
slowdowns with
reanalysis (ERA5)

19. INPUT
[DATA]
PREDICTION
Machine
Learning

20. INPUT
[DATA]
PREDICTION
Machine
Learning

21. INPUT
[DATA]
PREDICTION
Machine
Learning
Explainable AI
Learn new
science!

22. OCEAN HEAT CONTENT – 100 M
Start with anomalous ocean heat…

23. OCEAN HEAT CONTENT – 100 M
INPUT LAYER
Start with anomalous ocean heat…

24. OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
Will a slowdown begin?

25. OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
LAYER-WISE RELEVANCE PROPAGATION
Will a slowdown begin?

26. So how well does the neural network do?

27. Low High Colder Warmer

28. Low High Colder Warmer

29. Low High Colder Warmer

30. What about observations?
Future (2012-)
so-called “hiatus”
Comparing
observations to
the IPO

31. What about observations?
Future (2012-)
so-called “hiatus”
2021 (preliminary)
Looking ahead
to the near-
future…
?

32. What about observations?
Colder Warmer
[2003, 2004] [2016, 2017]

33. KEY POINTS
Zachary Labe
zmlabe@rams.colostate.edu
@ZLabe
1. An artificial neural network predicts the onset of slowdowns in decadal warming trends of
global mean surface temperature
2. Transitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with
warming slowdown trends in CESM2-LE
3. Explainable AI reveals the neural network is leveraging tropical patterns of ocean heat content
anomalies to make its predictions