Machine learning for climate prediction at decadal and longer timescales

Labe, Z.M. Machine learning for climate prediction at decadal and longer timescales, Predictability, Predictions, and Applications Interface (PPAI) Panel, US CLIVAR, Boulder, CO, USA (Aug 2024) (Invited-Remote).

References...
Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119

Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173

1. Machine learning for climate
prediction at decadal and
longer timescales
https://zacklabe.com/ @ZLabe
Zachary M. Labe
NOAA Geophysical Fluid Dynamics Laboratory (GFDL)
15 August 2024
PPAI Panel Meeting
US CLIVAR

3. By utilizing climate model large ensembles,
machine learning can reveal real-world
patterns of time-evolving forced change.
KEY MESSAGE

4. Machine Learning
is not new!
But…

5. Machine Learning
is not new!
“A Bayesian Neural Network for
Severe-Hail Prediction (2000)”
“Classification of Convective Areas
Using Decision Trees (2009)”
“A Neural Network for Damaging
Wind Prediction (1998)”
“Generative Additive Models versus
Linear Regression in Generating
Probabilistic MOS Forecasts of
Aviation Weather Parameters (1995)”
”A Neural Network for
Tornado Prediction
Based on Doppler
Radar-Derived
Attributes (1996)”
”The Diagnosis of
Upper-Level Humidity
(1968)”

6. “An adaptive data processing system for weather forecasting”
It’s a neural network!
[Hu and Root (1964), APME]

7. Machine learning for meteorology
IDENTIFYING SEVERE THUNDERSTORMS
Molina et al. 2021
Martin et al. 2022
CLASSIFYING PHASE OF MADDEN -JULLIAN OSCILLATION
DETECTING CONVECTION FROM SATELLITES
Lee et al. 2021
LOCATING COLD FRONTS
Dagon et al. 2022

8. Machine learning for climate
PHYSICAL DRIVERS OF ENSO DYNAMICS
Shin et al. 2022
IDENTIFYING DECADAL STATE DEPENDENCE
Gordon and Barnes, 2022
INTERNAL/EXTERNAL CLIMATE FORCING
Sweeney et al. 2023

9. INPUT PREDICTION

10. INPUT
[DATA]
PREDICTION
Machine
Learning

11. INPUT
[DATA]
PREDICTION
~Statistical
Algorithm~

12. INPUT
[DATA]
PREDICTION
Machine
Learning
Opening the black box

14. 5-year
lowess smoothing

15. NASA/GISS/GISTEMPv4
“Hiatus”

16. Global Warming
Hiatus?
…in research

17. Global Warming
Hiatus?
…in research

18. Global Warming
Hiatus?
…in research

19. Global Warming
Hiatus?
…in research

20. Global Warming
Hiatus?
…in research

21. Global Warming
Hiatus?
…in research

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

23. Global Warming
Accelerating?

24. Member #8
20CRv3
ERA5
Hiatus?

25. Member #8
20CRv3
ERA5
Hiatus?
Using neural networks to
predict decadal slowdowns
and accelerations
in global mean surface
temperature?

26. 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

27. Select one ensemble
member and calculate
the annual mean
global mean surface
temperature (GMST)
2-m TEMPERATURE
ANOMALY
[Labe and Barnes, 2022; GRL]

28. Calculate 10-year
moving (linear) trends
2-m TEMPERATURE
ANOMALY
[Labe and Barnes, 2022; GRL]

29. Plot the slope of the
linear trends
START OF 10-YEAR
TEMPERATURE TREND
2-m TEMPERATURE
ANOMALY
[Labe and Barnes, 2022; GRL]

30. Calculate a threshold
for defining a slowdown
in decadal warming
[Labe and Barnes, 2022; GRL]

31. Repeat this exercise for
each ensemble
member in CESM2-LE
[Labe and Barnes, 2022; GRL]

32. Compare warming
slowdowns with
reanalysis (ERA5)
[Labe and Barnes, 2022; GRL]

33. OCEAN HEAT CONTENT – 100 M
Start with anomalous ocean heat…
[Labe and Barnes, 2022; GRL]

34. OCEAN HEAT CONTENT – 100 M
INPUT LAYER
Start with anomalous ocean heat…
[Labe and Barnes, 2022; GRL]

35. OCEAN HEAT CONTENT – 100 M
INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
YES
SLOWDOWN
NO
SLOWDOWN
Will a slowdown begin?
[Labe and Barnes, 2022; GRL]

36. 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?
[Labe and Barnes, 2022; GRL]

37. Visualizing something we already know…

38. Neural
Network
[0] La Niña [1] El Niño
[Toms et al. 2020, JAMES]
Input a map of sea surface temperatures

39. Visualizing something we already know…
Input maps of sea surface
temperatures to identify
El Niño or La Niña
Use ‘LRP’ to see how the
neural network is making
its decision
[Toms et al. 2020, JAMES]
Layer-wise Relevance Propagation
Composite Observations
LRP [Relevance]
SST Anomaly [°C]
0.00 0.75
0.0 1.5-1.5

40. EXPLAINABLE AI (XAI)
1.Is the prediction correct for the right reasons?
• Is it consistent with our physical understanding of the climate system?
2.Provide insights for improving the machine learning model
• Is the model overfitting? Can the model be further optimized?
3.Learn new science
• For example, in climate prediction this could be a new forecast of opportunity or teleconnection
https://doi.org/10.1175/AIES-D-22-0001.1

41. EXPLAINABLE AI (XAI)
THERE ARE MANY
METHODS
A bird!
XAI
[Adapted from Adebayo et al., 2020]

42. THERE ARE MANY
METHODS
EXPLAINABLE AI (XAI)
[Adapted from Adebayo et al., 2020]

43. 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?
[Labe and Barnes, 2022; GRL]

44. Low High Colder Warmer
[Labe and Barnes, 2022; GRL]

45. WarmerColder
[Labe and Barnes, 2022; GRL]
Yellow Contours = XAI Attribution
Accurate predictions for a hiatus in global temperature
Ocean Heat Content (0-100 m; Normalized)
XAI reveals important areas of anomalous
ocean heat used to predict decadal
slowdowns, but it doesn’t tell us how or why
it leveraged this spatial information together

46. Low High Colder Warmer
[Labe and Barnes, 2022; GRL]

47. What about observations?
Future (2012-)so-call ed “hiatus ”
Comparing
observations
with the IPO
Index
[Labe and Barnes, 2022; GRL]

48. What about observations?
Future (2012-)so-call ed “hiatus ”
202 1
Looking ahead
to the near-
future…
?
202 2
Early 2000s
slowdown
202 3

49. What about observations?
Colder Warmer
[2003, 2004] [2016, 2017]
[Labe and Barnes, 2022; GRL]
LRP Relevance

50. Earth is also warming
in the vertical!
Po-Chedley, S., J.T. Fasullo, N. Siler,Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal
variability and forcing influence model-satellite differences in the rate of tropical tropospheric
warming.Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119

51. Adapted from
Peings

et al. 2018,
ERL
AA
UTW
LENS
Stratosphere
Troposphere
2100-2070
minus
1981-2010
Antarctic Equator Arctic
CLIMATE MODEL PROJECTION

52. OBSERVATIONS (ERA5)

53. https://www.realclimate.org/index.php/climate-model-projections-compared-to-observations/
Climate models exhibit 2x as much
warming as observations…
TMT = tropical mid-troposphere

54. PREDICT:
INTERNAL + EXTERNAL
COMPONENTS
[Po-Chedley et al. 2022, PNAS]

55. [Po-Chedley et al. 2022, PNAS]
Partial least squares regression with CMIP6 large ensembles (test observations)

56. [Po-Chedley et al. 2022, PNAS]
UNDERSTANDING OUR
PREDICTIONS
–
Patterns of internal variability
(e.g., Interdecadal Pacific
Oscillation)

57. [Po-Chedley et al. 2022, PNAS]

58. INPUT
[DATA]
PREDICTION
Machine
Learning
Explainable (or interpretable) AI
Learn new
climate science!

59. TAKEAWAYS
Po-Chedley, S., J.T. Fasullo, N. Siler,Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal
variability and forcing influence model-satellite differences in the rate of tropical tropospheric
warming.Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119
Labe, Z.M.and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with
explainable neural networks.Geophysical Research Letters, DOI:10.1029/2022GL098173
Zachary Labe
[email protected]
1.Artificial neural network predicts the onset of slowdowns in decadal warming trends of global
mean temperature
2.Explainable AI reveals the neural network is leveraging tropical patterns of ocean heat content
anomalies
3.Transitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with
warming slowdown trends in CESM2-LE