23 February 2024… Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ. References: Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464 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 Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981

1. APPLICATIONS OF MACHINE LEARNING
FOR
CLIMATE CHANGE AND VARIABILITY
https://zacklabe.com/ @ZLabe
Zachary M. Labe
Postdoc at NOAA GFDL and Princeton University; Atmospheric and Oceanic Science
with Elizabeth A. Barnes (CSU), Thomas L. Delworth (GFDL), Nathaniel C. Johnson (GFDL)
23 February 2024
Seminar at Rutgers University
Department of Environmental Sciences

2. 1) Where do we
go from here?

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

4. 3) How do we
account for
regional
patterns of
change?

5. Explainable machine learning can
distinguish between regional patterns
of time-evolving climate change
TAKEAWAY MESSAGE

6. Programming Languages Python – Machine Learning Libraries
https://insights.stackoverflow.com/trends

7. Machine Learning
is not new!
But…

8. 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)”

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

10. Artificial Intelligence
Machine Learning
Deep Learning
Computer/Data Science

11. Computer/Data Science
Supervised
Learning
Unsupervised
Learning
Labeled data
Classification
Regression
Unlabeled data
Clustering
Dimension reduction
Artificial Intelligence
Machine Learning
Deep Learning
DATA-HUNGRY!

12. Do it better
e.g., parameterizations in climate models are not
perfect, use ML to make them more accurate
Do it faster
e.g., code in climate models is very slow (but we
know the right answer) - use ML methods to speed
things up
Do something new
• e.g., go looking for non-linear relationships you
didn’t know were there
WHY ELSE SHOULD WE CONSIDER
MACHINE LEARNING?

13. Do it better
e.g., parameterizations in climate models are not
perfect, use ML to make them more accurate
Do it faster
e.g., code in climate models is very slow (but we
know the right answer) - use ML methods to speed
things up
Do something new
• e.g., go looking for non-linear relationships you
didn’t know were there
Very relevant for
research: may be
slower and worse,
but can still learn
something
WHY ELSE SHOULD WE CONSIDER
MACHINE LEARNING?

14. 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

15. Machine learning for oceanography
CLASSIFYING ARCTIC OCEAN ACIDIFICATION
Krasting et al. 2022
LARGE-SCALE OCEAN CIRCULATION
Clare et al. 2022
ESTIMATING OCEAN SURFACE CURRENTS
Sinha and Abernathey, 2021

16. 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
Po-Chedley et al. 2022

17. INPUT
[DATA]
PREDICTION
Machine
Learning

18. INPUT
[DATA]
PREDICTION
~Statistical
Algorithm~

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

20. Artificial Intelligence
Machine Learning
Deep Learning
Artificial Neural Networks
Computer/Data Science

21. X
Y
Our data

22. X
Y
Our data
Just an exercise in curve fitting… (overfitting!)

23. X1
X2
INPUTS
Artificial Neural Networks [ANN]

24. Linear regression!
Artificial Neural Networks [ANN]
X1
X2
W1
W2
∑ = X1W1+ X2W2 + b
INPUTS
NODE

25. Artificial Neural Networks [ANN]
X1
X2
W1
W2
∑
INPUTS
NODE
Linear regression with non-linear
mapping by an “activation function”
Training of the network is merely
determining the weights “w” and
bias/offset “b"
= factivation(X1W1+ X2W2 + b)

26. Artificial Neural Networks [ANN]
X1
X2
W1
W2
∑
INPUTS
NODE
= factivation(X1W1+ X2W2 + b)
ReLU Sigmoid Linear

27. X1
X2
∑
inputs
HIDDEN LAYERS
X3
∑
∑
∑
OUTPUT
= predictions
Artificial Neural Networks [ANN]
: : ::
INPUTS

28. Complexity and nonlinearities of the ANN allow it to learn
many different pathways of predictable behavior
Once trained, you have an array of weights and biases
which can be used for prediction on new data
INPUT
[DATA]
PREDICTION
Artificial Neural Networks [ANN]

29. TEMPERATURE

30. TEMPERATURE
We know some metadata…
+ What year is it?
+ Where did it come from?

31. We know some metadata…
+ What year is it?
+ Where did it come from?
[Labe and Barnes, 2022; ESS]
TEMPERATURE

32. TEMPERATURE
Neural network learns nonlinear
combinations of forced climate
patterns to identify the year
We know some metadata…
+ What year is it?
+ Where did it come from?
[Labe and Barnes, 2022; ESS]

33. ----ANN----
2 Hidden Layers
10 Nodes each
Ridge Regularization
Early Stopping
[e.g., Barnes et al. 2019, 2020]
[e.g., Labe and Barnes, 2021]
TIMING OF EMERGENCE
(COMBINED VARIABLES)
RESPONSES TO
EXTERNAL CLIMATE
FORCINGS
PATTERNS OF
CLIMATE INDICATORS
[e.g., Rader et al. 2022]
Surface Temperature Map Precipitation Map
+
TEMPERATURE
We know some metadata…
+ What year is it?
+ Where did it come from?
[Labe and Barnes, 2022; ESS]

34. ----ANN----
2 Hidden Layers
10 Nodes each
Ridge Regularization
Early Stopping
[e.g., Barnes et al. 2019, 2020]
[e.g., Labe and Barnes, 2021]
TIMING OF EMERGENCE
(COMBINED VARIABLES)
RESPONSES TO
EXTERNAL CLIMATE
FORCINGS
PATTERNS OF
CLIMATE INDICATORS
Surface Temperature Map Precipitation Map
+
TEMPERATURE
[e.g., Rader et al. 2022]
We know some metadata…
+ What year is it?
+ Where did it come from?
[Labe and Barnes, 2022; ESS]

35. THE REAL WORLD
(Observations)
What is the annual mean temperature of Earth?

36. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
Anomaly is relative to 1951-1980

37. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
Let’s run a
climate model

38. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
Let’s run a
climate model
again

39. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
Let’s run a
climate model
again & again

40. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
CLIMATE MODEL
ENSEMBLES

41. What is the annual mean temperature of Earth?
THE REAL WORLD
(Observations)
CLIMATE MODEL
ENSEMBLES
Range of ensembles
= internal variability (noise)
Mean of ensembles
= forced response (climate change)

42. What is the annual mean temperature of Earth?
Range of ensembles
= internal variability (noise)
Mean of ensembles
= forced response (climate change)
But let’s remove
climate change…

43. What is the annual mean temperature of Earth?
Range of ensembles
= internal variability (noise)
Mean of ensembles
= forced response (climate change)
After removing the
forced response…
anomalies/noise!

44. What is the annual mean temperature of Earth?
• Increasing greenhouse gases (CO2, CH4, N2O)
• Changes in industrial aerosols (SO4, BC, OC)
• Changes in biomass burning (aerosols)
• Changes in land-use & land-cover (albedo)

45. What is the annual mean temperature of Earth?
• Increasing greenhouse gases (CO2, CH4, N2O)
• Changes in industrial aerosols (SO4, BC, OC)
• Changes in biomass burning (aerosols)
• Changes in land-use & land-cover (albedo)
Plus everything else…
(Natural/internal variability)

46. What is the annual mean temperature of Earth?

47. Greenhouse gases fixed to 1920 levels
All forcings (CESM-LE)
Industrial aerosols fixed to 1920 levels
[Deser et al. 2020, JCLI]
Fully-coupled CESM1.1
20 Ensemble Members
Run from 1920-2080
Observations

48. So what?
Greenhouse gases = warming
Aerosols = ?? (though mostly cooling)
What are the relative responses
between greenhouse gas
and aerosol forcing?

49. Surface Temperature Map
ARTIFICIAL NEURAL NETWORK (ANN)
Input data from one of the
three single forcing large
ensemble simulations
(AER+, GHG+, ALL)

50. INPUT LAYER
Surface Temperature Map
ARTIFICIAL NEURAL NETWORK (ANN)

51. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
ARTIFICIAL NEURAL NETWORK (ANN)

52. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
ARTIFICIAL NEURAL NETWORK (ANN)

53. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Layer-wise Relevance Propagation
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
ARTIFICIAL NEURAL NETWORK (ANN)
[Barnes et al. 2020, JAMES]
[Labe and Barnes 2021, JAMES]

54. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
WHY
WHY
WHY
Backpropagation – LRP

55. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
WHY
WHY
WHY
Backpropagation – LRP

56. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Volcano
Great White
Shark
Timber
Wolf
Image Classification LRP
https://heatmapping.org/
LRP heatmaps show regions
of “relevance” that
contribute to the neural
network’s decision-making
process for a sample
belonging to a particular
output category
Neural Network
Backpropagation – LRP
WHY
WHY
WHY

57. LAYER-WISE RELEVANCE PROPAGATION (LRP)
Image Classification LRP
https://heatmapping.org/
NOT PERFECT
Crock
Pot
Neural Network
Backpropagation – LRP
WHY

58. [Adapted from Adebayo et al., 2020]
EXPLAINABLE AI (XAI) IS
NOT PERFECT
THERE ARE MANY
METHODS
A bird!
XAI

59. [Adapted from Adebayo et al., 2020]
THERE ARE MANY
METHODS
EXPLAINABLE AI (XAI) IS
NOT PERFECT

60. Visualizing something we already know…
ENSO

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

62. Visualizing something we already know…
Input maps of sea surface
temperatures (SST) 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 SST Observations
LRP [Relevance]
SST Anomaly [°C]
0.00 0.75
0.0 1.5
-1.5

63. INPUT LAYER
HIDDEN LAYERS
OUTPUT LAYER
Layer-wise Relevance Propagation
Surface Temperature Map
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
ARTIFICIAL NEURAL NETWORK (ANN)
[Barnes et al. 2020, JAMES]
[Labe and Barnes 2021, JAMES]

64. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
Warming
Cooling

65. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
Warming
Cooling

66. 1960-1999: ANNUAL MEAN TEMPERATURE TRENDS
Greenhouse gases fixed
to 1920 levels
[AEROSOLS PREVAIL]
Industrial aerosols fixed
to 1920 levels
[GREENHOUSE GASES PREVAIL]
All forcings
[STANDARD CESM-LE]
DATA
Warming
Cooling

67. CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]

68. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]

69. OBSERVATIONS
SLOPES
PREDICT THE YEAR FROM MAPS OF TEMPERATURE
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL
[Labe and Barnes 2021, JAMES]

70. Higher LRP values indicate greater relevance
for the ANN’s prediction
AVERAGED OVER 1960-2039
Aerosol-driven
Greenhouse gas-driven
All forcings
Low High
[Labe and Barnes 2021, JAMES]

71. KEY POINTS FROM EXAMPLE #1
1. Using explainable AI methods with artificial neural networks (ANN)
reveals climate patterns in large ensemble simulations
2. A metric is proposed for quantifying the uncertainty of an ANN
visualization method that extracts signals from different external
forcings
3. Predictions from an ANN trained using a large ensemble without
time-evolving aerosols show the highest correlation with actual
observations
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing
large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464

72. Seasonal Maps of T2M, TMAX, TMIN
Input
NOAA GFDL – SPEAR_MED
Fully-Coupled (AM4/LM4/MOM6/SIS2)
Historical + SSP5-8.5
0.5° land/atmosphere, 1.0° ocean
https://www.gfdl.noaa.gov/spear/
Delworth et al. (2020, JAMES)

73. Seasonal Maps of T2M, TMAX, TMIN
Hidden Layers
Artificial Neural Network
Input

74. Backpropagation
Seasonal Maps of T2M, TMAX, TMIN
Hidden Layers
Output
1921
2100
Artificial Neural Network
Input
Post hoc – XAI methods

75. A "warming hole”

76. Temperature anomalies [ °C ] relative to 1981-2010
Observations from NClimGrid
Climate model data from GFDL SPEAR_MED
United States – Summer
1920 2020

77. Temperature anomalies [ °C ] relative to 1981-2010
United States – Summer
1920 2020
Dust Bowl – July 1936
Mt. Pinatubo
2022

78. Eischeid, J. K., Hoerling, M. P., Quan, X. W., Kumar, A., Barsugli, J., Labe,
Z. M., ... & Zhang, X. (2023). Why Has the Summertime Central US
Warming Hole Not Disappeared? Journal of Climate, 36(20), 7319-7336.
https://doi.org/10.1175/JCLI-D-22-0716.1
Persistence is consistent with
unusually high summertime
rainfall over the region
Large ensembles demonstrate
that this rainfall trend can arise
from atmospheric internal
variability alone
Recent trend in tropical Pacific
SST can also reinforce this
pattern

79. Neural Network
Predictions
for SPEAR/Obs

80. 1921 2021 2100
ACTUAL YEARS
PREDICTED
YEARS
NOAA Monthly U.S.
NClimGrid v1.0
1921
2021
2100

81. Max year predicted in the 1921-1950
baseline for observations
Timing of
Emergence
1921-1950
Skill
1:1 Perfect Prediction

82. June – August – Timing of Emergence (ToE) For Observations Over United States

83. How is the neural network able to detect the year prior to ~1990?
Temperature anomalies [ °C ] relative to 1981-2010
Machine learning predictions GFDL SPEAR_MED simulation

84. Machine Learning Explainability Methods – Ad Hoc Feature Attribution
Decrease
likelihood of year
Increase
likelihood of year

85. Western USA Central USA Eastern USA

86. Western USA Central USA Eastern USA

87. Western USA Central USA Eastern USA

88. 1) Is it
aerosols?
Only available from
1921 to 2020
(all forcings)
(all forcings, but no anthropogenic aerosols)

89. Coherence
Check!
(all forcings)
(a natural-only forcing simulation)

90. 50 km resolution 100 km resolution
2) Is it related to resolution?
SPEAR_MED SPEAR_LO
MAE
(years)
Mean Absolute Error (MAE) for ensemble member predictions over 1921 to 1989 for different spatial resolutions

91. 3) Is it systematic in CMIP6?

92. 4) So, what is it? The land surface?

93. TRENDS FROM 1921 TO 1950
SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
Warmer
Colder
Fully-Coupled [Historical]

94. TRENDS FROM 1921 TO 1950
SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
Warmer
Colder
Fully-Coupled [Historical]

95. TRENDS FROM 1921 TO 1950
Fully-Coupled [Historical] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
Warmer
Colder

96. KEY POINTS FROM EXAMPLE #2
1.Forced temperature changes have emerged in observations
during summer in the United States as detected by an ANN
2.Increasing spatial resolution improves neural network skill for
predicting the year of a given summer temperature map
3.Western United States land surface climate properties
contribute to earlier timing of emergence predictions for the
SPEAR climate model
Labe, Z.M., N.C. Johnson, and T.L Delworth (2024). Changes in United States summer temperatures
revealed by explainable neural networks, Earth’s Future, DOI: 10.1029/2023EF003981

98. 5-year
lowess smoothing

99. NASA/GISS/GISTEMPv4
“Hiatus”

100. Global Warming
Hiatus?
…in research

101. Global Warming
Hiatus?
…in research

102. Global Warming
Hiatus?
…in research

103. Global Warming
Hiatus?
…in research

104. Global Warming
Hiatus?
…in research

105. Global Warming
Hiatus?
…in research

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

107. 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

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

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

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

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

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

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

114. [Labe and Barnes, 2022; GRL]

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

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

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

118. 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]

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

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

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

122. What about observations?
Future (2012-)
so-called “hiatus”
2021
Looking ahead
to the near-
future…
?
2022
Early 2000s
slowdown
2023

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

124. KEY POINTS FROM EXAMPLE #3
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
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

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

126. MACHINE LEARNING IS JUST
ANOTHER TOOL TO ADD TO OUR
WORKFLOW.
1)

127. MACHINE LEARNING IS
NO LONGER A BLACK BOX.
2)

128. WE CAN LEARN NEW SCIENCE
FROM EXPLAINABLE AI.
3)

129. TAKEAWAYS
1. Machine learning is just another tool to consider for our scientific workflow
2. We can use explainable AI (XAI) methods to peer into the black box of machine learning
3. We can learn new science by using XAI methods in conjunction with existing statistical tools
Zachary Labe
zachary.labe@noaa.gov
Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing
large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464
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
Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures
revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981