Zachary Labe, profile picture
Uploaded byZachary Labe
40 views

Exploring explainable machine learning for detecting changes in climate

The document discusses the application of machine learning in climate science, particularly focusing on its ability to detect and analyze climate changes. It highlights the potential of explainable artificial intelligence (AI) techniques, such as artificial neural networks, to reveal deep insights into climate patterns and variability. Furthermore, it emphasizes the importance of using machine learning to enhance the accuracy and efficiency of climate models while addressing uncertainties in climate predictions.

Embed presentation

Download to read offline
1 / 113
2 / 113
3 / 113
4 / 113
5 / 113
6 / 113
7 / 113
8 / 113
9 / 113
10 / 113
11 / 113
12 / 113
13 / 113
14 / 113
15 / 113
16 / 113
17 / 113
18 / 113
19 / 113
20 / 113
21 / 113
22 / 113
23 / 113
24 / 113
25 / 113
26 / 113
27 / 113
28 / 113
29 / 113
30 / 113
31 / 113
32 / 113
33 / 113
34 / 113
35 / 113
36 / 113
37 / 113
38 / 113
39 / 113
40 / 113
41 / 113
42 / 113
43 / 113
44 / 113
45 / 113
46 / 113
47 / 113
48 / 113
49 / 113
50 / 113
51 / 113
52 / 113
53 / 113
54 / 113
55 / 113
56 / 113
57 / 113
58 / 113
59 / 113
60 / 113
61 / 113
62 / 113
63 / 113
64 / 113
65 / 113
66 / 113
67 / 113
68 / 113
69 / 113
70 / 113
71 / 113
72 / 113
73 / 113
74 / 113
75 / 113
76 / 113
77 / 113
78 / 113
79 / 113
80 / 113
81 / 113
82 / 113
83 / 113
84 / 113
85 / 113
86 / 113
87 / 113
88 / 113
89 / 113
90 / 113
91 / 113
92 / 113
93 / 113
94 / 113
95 / 113
96 / 113
97 / 113
98 / 113
99 / 113
100 / 113
101 / 113
102 / 113
103 / 113
104 / 113
105 / 113
106 / 113
107 / 113
108 / 113
109 / 113
110 / 113
111 / 113
112 / 113
113 / 113
EXPLORING EXPLAINABLE MACHINE LEARNING FOR DETECTING CHANGES IN CLIMATE https://zacklabe.com/ @ZLabe Zachary M. Labe NOAA GFDL and Princeton University; Atmospheric and Oceanic Sciences 9 February 2023 Florida State University Department of Earth, Ocean, and Atmospheric Science
Machine Learning is not new! But…
Machine Learning is not new!
Artificial Intelligence Machine Learning Deep Learning Computer Science
Computer Science Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Labeled data Classification Regression Unlabeled data Clustering Dimension reduction
• 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 SHOULD WE CONSIDER MACHINE LEARNING?
Machine learning for weather IDENTIFYING SEVERE THUNDERSTORMS Molina et al. 2021 Toms et al. 2021 CLASSIFYING PHASE OF MADDEN-JULLIAN OSCILLATION SATELLITE DETECTION Lee et al. 2021 DETECTING TORNADOES McGovern et al. 2019
Machine learning for climate FINDING FORECASTS OF OPPORTUNITY Mayer and Barnes, 2021 PREDICTING CLIMATE MODES OF VARIABILITY Gordon et al. 2021 TIMING OF CLIMATE CHANGE Barnes et al. 2019
Machine learning for oceanography CLASSIFYING ARCTIC OCEAN ACIDIFICATION Krasting et al. 2022 TRACK AND REVEAL DEEP WATER MASSES Sonnewald and Lguensat, 2021 ESTIMATING OCEAN SURFACE CURRENTS Sinha and Abernathey, 2021
INPUT [DATA] PREDICTION Machine Learning
INPUT [DATA] PREDICTION ~Statistical Algorithm~
INPUT [DATA] PREDICTION Machine Learning
Artificial Intelligence Machine Learning Deep Learning
X1 X2 INPUTS Artificial Neural Networks [ANN]
Linear regression! Artificial Neural Networks [ANN] X1 X2 W1 W2 ∑ = X1W1+ X2W2 + b INPUTS NODE
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)
Artificial Neural Networks [ANN] X1 X2 W1 W2 ∑ INPUTS NODE = factivation(X1W1+ X2W2 + b) ReLU Sigmoid Linear
X1 X2 ∑ inputs HIDDEN LAYERS X3 ∑ ∑ ∑ OUTPUT = predictions Artificial Neural Networks [ANN] : : :: INPUTS
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]
TEMPERATURE
TEMPERATURE We know some metadata… + What year is it? + Where did it come from?
We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS] TEMPERATURE
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]
----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]
----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]
THE REAL WORLD (Observations) What is the annual mean temperature of Earth?
What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model
What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again
What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again & again
What is the annual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES
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)
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…
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!
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)
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)
What is the annual mean temperature of Earth?
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
So what? Greenhouse gases = warming Aerosols = ?? (though mostly cooling) What are the relative responses between greenhouse gas and aerosol forcing?
Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
INPUT LAYER Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
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)
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]
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
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
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
LAYER-WISE RELEVANCE PROPAGATION (LRP) Image Classification LRP https://heatmapping.org/ NOT PERFECT Crock Pot Neural Network Backpropagation – LRP WHY
[Adapted from Adebayo et al., 2020] EXPLAINABLE AI IS NOT PERFECT THERE ARE MANY METHODS
[Adapted from Adebayo et al., 2020] THERE ARE MANY METHODS EXPLAINABLE AI IS NOT PERFECT
Visualizing something we already know…
Neural Network [0] La Niña [1] El Niño [Toms et al. 2020, JAMES] Input a map of sea surface temperatures
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
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]
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
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
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
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
CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
OBSERVATIONS SLOPES PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
HOW DID THE ANN MAKE ITS PREDICTIONS?
HOW DID THE ANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?
RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
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]
Greenhouse gas-driven Aerosol-driven All forcings AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
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
NASA/GISS/GISTEMPv4 “Hiatus”
Global Warming Hiatus? …in research
Global Warming Hiatus? …in research
Global Warming Hiatus? …in research
Global Warming Hiatus? …in research
Global Warming Hiatus? …in research
Global Warming Hiatus? …in research
Global Warming Hiatus? …in the media, etc.
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
Select one ensemble member and calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
Calculate 10-year moving (linear) trends 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
Plot the slope of the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
Calculate a threshold for defining a slowdown in decadal warming [Labe and Barnes, 2022; GRL]
Repeat this exercise for each ensemble member in CESM2-LE [Labe and Barnes, 2022; GRL]
Compare warming slowdowns with reanalysis (ERA5) [Labe and Barnes, 2022; GRL]
[Labe and Barnes, 2022; GRL]
OCEAN HEAT CONTENT – 100 M Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]
OCEAN HEAT CONTENT – 100 M INPUT LAYER Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]
OCEAN HEAT CONTENT – 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN Will a slowdown begin? [Labe and Barnes, 2022; GRL]
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]
So how well does the neural network do? [Labe and Barnes, 2022; GRL]
Low High Colder Warmer [Labe and Barnes, 2022; GRL]
Low High Colder Warmer [Labe and Barnes, 2022; GRL]
Low High Colder Warmer [Labe and Barnes, 2022; GRL]
What about observations? Future (2012-) so-called “hiatus” Comparing observations to the IPO [Labe and Barnes, 2022; GRL]
What about observations? Future (2012-) so-called “hiatus” 2021 Looking ahead to the near- future… ?
What about observations? Colder Warmer [2003, 2004] [2016, 2017] [Labe and Barnes, 2022; GRL]
KEY POINTS FROM EXAMPLE #2 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
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
Adapted from Peings et al. 2018, ERL AA UTW LENS Stratosphere Troposphere 2100-2070 minus 1981-2010 Antarctic Equator Arctic CLIMATE MODEL PROJECTION
OBSERVATIONS
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
[Po-Chedley et al. 2022, PNAS] PREDICT: INTERNAL + EXTERNAL COMPONENTS
[Po-Chedley et al. 2022, PNAS] Partial least squares regression with CMIP6 large ensembles (test observations)
[Po-Chedley et al. 2022, PNAS] UNDERSTANDING OUR PREDICTIONS – Patterns of internal variability (e.g., Interdecadal Pacific Oscillation)
[Po-Chedley et al. 2022, PNAS]
INPUT [DATA] PREDICTION Machine Learning Explainable AI Learn new science!
MACHINE LEARNING IS JUST ANOTHER TOOL TO ADD TO OUR WORKFLOW. 1)
MACHINE LEARNING IS NO LONGER A BLACK BOX. 2)
WE CAN LEARN NEW SCIENCE FROM EXPLAINABLE AI. 3)
KEY POINTS 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 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

More Related Content

PDF
Exploring climate change signals with explainable AI
byZachary Labe
 
PDF
Using explainable machine learning for evaluating patterns of climate change
byZachary Labe
 
PDF
Learning new climate science by opening the machine learning black box
byZachary Labe
 
PDF
Forced climate signals with explainable AI and large ensembles
byZachary Labe
 
PDF
Applications of machine learning for climate change and variability
byZachary Labe
 
PDF
Machine learning for evaluating climate model projections
byZachary Labe
 
PDF
Using explainable machine learning to evaluate climate change projections
byZachary Labe
 
PDF
Assessing climate variability and change with explainable neural networks
byZachary Labe
 
Exploring climate change signals with explainable AI
byZachary Labe
 
Using explainable machine learning for evaluating patterns of climate change
byZachary Labe
 
Learning new climate science by opening the machine learning black box
byZachary Labe
 
Forced climate signals with explainable AI and large ensembles
byZachary Labe
 
Applications of machine learning for climate change and variability
byZachary Labe
 
Machine learning for evaluating climate model projections
byZachary Labe
 
Using explainable machine learning to evaluate climate change projections
byZachary Labe
 
Assessing climate variability and change with explainable neural networks
byZachary Labe
 

Similar to Exploring explainable machine learning for detecting changes in climate

PDF
Learning new climate science by thinking creatively with machine learning
byZachary Labe
 
PDF
Using explainable AI to identify key regions of climate change in GFDL SPEAR ...
byZachary Labe
 
PDF
Explainable AI for identifying regional climate change patterns
byZachary Labe
 
PDF
Explainable AI approach for evaluating climate models in the Arctic
byZachary Labe
 
PDF
Creative machine learning approaches for climate change detection
byZachary Labe
 
PDF
Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainab...
byZachary Labe
 
PDF
Using machine learning for understanding changes in the Earth system
byZachary Labe
 
PDF
Explainable neural networks for evaluating patterns of climate change and var...
byZachary Labe
 
PDF
Evaluating global climate models using simple, explainable neural networks
byZachary Labe
 
PDF
Using neural networks to explore regional climate patterns in single-forcing ...
byZachary Labe
 
PDF
XAI for detecting scenarios of future climate change
byZachary Labe
 
PDF
Using explainable neural networks for comparing climate model projections
byZachary Labe
 
PDF
Using artificial neural networks to predict temporary slowdowns in global war...
byZachary Labe
 
PDF
Machine learning for climate prediction at decadal and longer timescales
byZachary Labe
 
PDF
An intro to explainable AI for polar climate science
byZachary Labe
 
PDF
Exploring climate model large ensembles with explainable neural networks
byZachary Labe
 
PDF
Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Net...
byZachary Labe
 
PDF
Explainable AI for distinguishing future climate change scenarios
byZachary Labe
 
PDF
Using neural networks to predict temporary slowdowns in decadal climate warmi...
byZachary Labe
 
PDF
Revealing climate change signals with explainable AI
byZachary Labe
 
Learning new climate science by thinking creatively with machine learning
byZachary Labe
 
Using explainable AI to identify key regions of climate change in GFDL SPEAR ...
byZachary Labe
 
Explainable AI for identifying regional climate change patterns
byZachary Labe
 
Explainable AI approach for evaluating climate models in the Arctic
byZachary Labe
 
Creative machine learning approaches for climate change detection
byZachary Labe
 
Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainab...
byZachary Labe
 
Using machine learning for understanding changes in the Earth system
byZachary Labe
 
Explainable neural networks for evaluating patterns of climate change and var...
byZachary Labe
 
Evaluating global climate models using simple, explainable neural networks
byZachary Labe
 
Using neural networks to explore regional climate patterns in single-forcing ...
byZachary Labe
 
XAI for detecting scenarios of future climate change
byZachary Labe
 
Using explainable neural networks for comparing climate model projections
byZachary Labe
 
Using artificial neural networks to predict temporary slowdowns in global war...
byZachary Labe
 
Machine learning for climate prediction at decadal and longer timescales
byZachary Labe
 
An intro to explainable AI for polar climate science
byZachary Labe
 
Exploring climate model large ensembles with explainable neural networks
byZachary Labe
 
Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Net...
byZachary Labe
 
Explainable AI for distinguishing future climate change scenarios
byZachary Labe
 
Using neural networks to predict temporary slowdowns in decadal climate warmi...
byZachary Labe
 
Revealing climate change signals with explainable AI
byZachary Labe
 

More from Zachary Labe

PDF
Sharing complex research through effective communication techniques
byZachary Labe
 
PDF
Data visualization for trusted science communication
byZachary Labe
 
PDF
Seeing global change through local climate data
byZachary Labe
 
PDF
Introduction to Arctic Disaster Risk Reduction
byZachary Labe
 
PDF
Bringing real-time climate monitoring to life
byZachary Labe
 
PDF
Visualizing our changing climate in real-time
byZachary Labe
 
PDF
Science in retreat: The consequences for the Arctic and our planet
byZachary Labe
 
PDF
Monitoring our changing climate through data-driven stories
byZachary Labe
 
PDF
Utility of explainable machine learning for scenarios of climate change
byZachary Labe
 
PDF
Climate Information for Society: Attribution and Engineering
byZachary Labe
 
PDF
The Arctic through the lens of data visualization
byZachary Labe
 
PDF
Our changing climate in the Northeast United States
byZachary Labe
 
PDF
Using machine learning for understanding changes in U.S. summertime temperatures
byZachary Labe
 
PDF
Timing of emergence of U.S. summer temperatures with explainable AI
byZachary Labe
 
PDF
Data Visualization Workshop for Summer Interns
byZachary Labe
 
PDF
Welcome to GFDL for Take Your Child To Work Day
byZachary Labe
 
PDF
Reexamining future projections of Arctic climate linkages
byZachary Labe
 
PDF
Techniques and Considerations for Improving Accessibility in Online Media
byZachary Labe
 
PDF
Using accessible data to communicate global climate change
byZachary Labe
 
PDF
Water in a Frozen Arctic: Cross-Disciplinary Perspectives
byZachary Labe
 
Sharing complex research through effective communication techniques
byZachary Labe
 
Data visualization for trusted science communication
byZachary Labe
 
Seeing global change through local climate data
byZachary Labe
 
Introduction to Arctic Disaster Risk Reduction
byZachary Labe
 
Bringing real-time climate monitoring to life
byZachary Labe
 
Visualizing our changing climate in real-time
byZachary Labe
 
Science in retreat: The consequences for the Arctic and our planet
byZachary Labe
 
Monitoring our changing climate through data-driven stories
byZachary Labe
 
Utility of explainable machine learning for scenarios of climate change
byZachary Labe
 
Climate Information for Society: Attribution and Engineering
byZachary Labe
 
The Arctic through the lens of data visualization
byZachary Labe
 
Our changing climate in the Northeast United States
byZachary Labe
 
Using machine learning for understanding changes in U.S. summertime temperatures
byZachary Labe
 
Timing of emergence of U.S. summer temperatures with explainable AI
byZachary Labe
 
Data Visualization Workshop for Summer Interns
byZachary Labe
 
Welcome to GFDL for Take Your Child To Work Day
byZachary Labe
 
Reexamining future projections of Arctic climate linkages
byZachary Labe
 
Techniques and Considerations for Improving Accessibility in Online Media
byZachary Labe
 
Using accessible data to communicate global climate change
byZachary Labe
 
Water in a Frozen Arctic: Cross-Disciplinary Perspectives
byZachary Labe
 

Recently uploaded

PDF
BIOREMEDIATION -MICROBIAL LEACHING OF ORES AND ITS PROCESS
byswethaviji2005
 
PDF
Betterszie | Exploring Dendrimer Generations Using the BeSEC
byBettersize Instruments
 
PDF
Machine Learning Lectures Multi layer Neural Networks
byZahra Sadeghi
 
PPT
Ch04uiuuuuuuuuuuuuuuuuuuuuuukkkkkkklh.ppt
bykarzanberdawood
 
PPTX
Computer vision lecture 1 - Copy (2).pptx
byCosmasKnowen
 
PPTX
Aerobic-and-Anaerobic-Digesters-Biological-Waste-Treatment-Technologies.pptx
byShanmugaPriya168919
 
PPTX
LOWER LIMB ANATOMY notes for GROSS ANATOMY
bymarkmutugi44
 
PDF
PHYTOREMEDIATION OF HEAVY METALS IN SOIL
byswethaviji2005
 
PDF
Alkene ( एल्कीन ) organic Chemistry Notes PDF Download - Irfanullah Mehar - W...
byWorld of Wisdom
 
PDF
Benzene ( बेंजीन ) - Organic Chemistry Notes PDF Download Slide - Irfanullah ...
byWorld of Wisdom
 
PDF
Alkane ( एल्केन ) - Notes PDF Download Hindi Medium - IRFANULLAH MEHAR.pdf
byWorld of Wisdom
 
PPTX
Annual Report of CMC filed in the USFDA for NDA & ANDA
byPHARMA IQ EDUCATION
 
PDF
Bioremedation - Dechlorination ,Types and process , advantage and disadvantage
byswathijagadeesan1402
 
PPTX
Light compensation point, CO2 compensation point, Quantum yield in plants.pptx
byRashmiMG2
 
PPTX
Radially polarized THz pulses by ZnTe axicon pumped at 2060 nm
bypolgyu16
 
PDF
Bettersize | Absolute Molecular Weight Determination of HSAs using the BeSEC
byBettersize Instruments
 
PDF
Machine Learning Lectures --- Likelihood
byZahra Sadeghi
 
PDF
Secondary waste water treatment using membrane bioreactor
bykavyaboopa07
 
PDF
S. Dharshini ( Msc. Microbiology) Microbial aspects and Metabolic aspects.
bydharshinisampath6
 
PDF
Origin Of Life & Evolution || BIOLOGY || CBSE & CHSE
byVivek Kola
 
BIOREMEDIATION -MICROBIAL LEACHING OF ORES AND ITS PROCESS
byswethaviji2005
 
Betterszie | Exploring Dendrimer Generations Using the BeSEC
byBettersize Instruments
 
Machine Learning Lectures Multi layer Neural Networks
byZahra Sadeghi
 
Ch04uiuuuuuuuuuuuuuuuuuuuuuukkkkkkklh.ppt
bykarzanberdawood
 
Computer vision lecture 1 - Copy (2).pptx
byCosmasKnowen
 
Aerobic-and-Anaerobic-Digesters-Biological-Waste-Treatment-Technologies.pptx
byShanmugaPriya168919
 
LOWER LIMB ANATOMY notes for GROSS ANATOMY
bymarkmutugi44
 
PHYTOREMEDIATION OF HEAVY METALS IN SOIL
byswethaviji2005
 
Alkene ( एल्कीन ) organic Chemistry Notes PDF Download - Irfanullah Mehar - W...
byWorld of Wisdom
 
Benzene ( बेंजीन ) - Organic Chemistry Notes PDF Download Slide - Irfanullah ...
byWorld of Wisdom
 
Alkane ( एल्केन ) - Notes PDF Download Hindi Medium - IRFANULLAH MEHAR.pdf
byWorld of Wisdom
 
Annual Report of CMC filed in the USFDA for NDA & ANDA
byPHARMA IQ EDUCATION
 
Bioremedation - Dechlorination ,Types and process , advantage and disadvantage
byswathijagadeesan1402
 
Light compensation point, CO2 compensation point, Quantum yield in plants.pptx
byRashmiMG2
 
Radially polarized THz pulses by ZnTe axicon pumped at 2060 nm
bypolgyu16
 
Bettersize | Absolute Molecular Weight Determination of HSAs using the BeSEC
byBettersize Instruments
 
Machine Learning Lectures --- Likelihood
byZahra Sadeghi
 
Secondary waste water treatment using membrane bioreactor
bykavyaboopa07
 
S. Dharshini ( Msc. Microbiology) Microbial aspects and Metabolic aspects.
bydharshinisampath6
 
Origin Of Life & Evolution || BIOLOGY || CBSE & CHSE
byVivek Kola
 

Exploring explainable machine learning for detecting changes in climate

  • 1.
    EXPLORING EXPLAINABLE MACHINE LEARNINGFOR DETECTING CHANGES IN CLIMATE https://zacklabe.com/ @ZLabe Zachary M. Labe NOAA GFDL and Princeton University; Atmospheric and Oceanic Sciences 9 February 2023 Florida State University Department of Earth, Ocean, and Atmospheric Science
  • 2.
  • 3.
  • 4.
  • 5.
    Computer Science Artificial Intelligence MachineLearning Deep Learning Supervised Learning Unsupervised Learning Labeled data Classification Regression Unlabeled data Clustering Dimension reduction
  • 6.
    • Do itbetter • 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 SHOULD WE CONSIDER MACHINE LEARNING?
  • 7.
    Machine learning forweather IDENTIFYING SEVERE THUNDERSTORMS Molina et al. 2021 Toms et al. 2021 CLASSIFYING PHASE OF MADDEN-JULLIAN OSCILLATION SATELLITE DETECTION Lee et al. 2021 DETECTING TORNADOES McGovern et al. 2019
  • 8.
    Machine learning forclimate FINDING FORECASTS OF OPPORTUNITY Mayer and Barnes, 2021 PREDICTING CLIMATE MODES OF VARIABILITY Gordon et al. 2021 TIMING OF CLIMATE CHANGE Barnes et al. 2019
  • 9.
    Machine learning foroceanography CLASSIFYING ARCTIC OCEAN ACIDIFICATION Krasting et al. 2022 TRACK AND REVEAL DEEP WATER MASSES Sonnewald and Lguensat, 2021 ESTIMATING OCEAN SURFACE CURRENTS Sinha and Abernathey, 2021
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
    Linear regression! Artificial NeuralNetworks [ANN] X1 X2 W1 W2 ∑ = X1W1+ X2W2 + b INPUTS NODE
  • 16.
    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)
  • 17.
    Artificial Neural Networks[ANN] X1 X2 W1 W2 ∑ INPUTS NODE = factivation(X1W1+ X2W2 + b) ReLU Sigmoid Linear
  • 18.
  • 19.
    Complexity and nonlinearitiesof 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]
  • 20.
  • 21.
    TEMPERATURE We know somemetadata… + What year is it? + Where did it come from?
  • 22.
    We know somemetadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS] TEMPERATURE
  • 23.
    TEMPERATURE Neural network learnsnonlinear 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]
  • 24.
    ----ANN---- 2 Hidden Layers 10Nodes 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]
  • 25.
    ----ANN---- 2 Hidden Layers 10Nodes 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]
  • 26.
    THE REAL WORLD (Observations) Whatis the annual mean temperature of Earth?
  • 27.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
  • 28.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model
  • 29.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again
  • 30.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again & again
  • 31.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES
  • 32.
    What is theannual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change)
  • 33.
    What is theannual mean temperature of Earth? Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) But let’s remove climate change…
  • 34.
    What is theannual mean temperature of Earth? Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) After removing the forced response… anomalies/noise!
  • 35.
    What is theannual 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)
  • 36.
    What is theannual 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)
  • 37.
    What is theannual mean temperature of Earth?
  • 38.
    Greenhouse gases fixedto 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
  • 39.
    So what? Greenhouse gases= warming Aerosols = ?? (though mostly cooling) What are the relative responses between greenhouse gas and aerosol forcing?
  • 40.
  • 41.
    INPUT LAYER Surface TemperatureMap ARTIFICIAL NEURAL NETWORK (ANN)
  • 42.
    INPUT LAYER HIDDEN LAYERS OUTPUTLAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
  • 43.
    INPUT LAYER HIDDEN LAYERS OUTPUTLAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI ARTIFICIAL NEURAL NETWORK (ANN)
  • 44.
    INPUT LAYER HIDDEN LAYERS OUTPUTLAYER 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]
  • 45.
    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
  • 46.
    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
  • 47.
    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
  • 48.
    LAYER-WISE RELEVANCE PROPAGATION(LRP) Image Classification LRP https://heatmapping.org/ NOT PERFECT Crock Pot Neural Network Backpropagation – LRP WHY
  • 49.
    [Adapted from Adebayoet al., 2020] EXPLAINABLE AI IS NOT PERFECT THERE ARE MANY METHODS
  • 50.
    [Adapted from Adebayoet al., 2020] THERE ARE MANY METHODS EXPLAINABLE AI IS NOT PERFECT
  • 51.
    Visualizing something wealready know…
  • 52.
    Neural Network [0] La Niña[1] El Niño [Toms et al. 2020, JAMES] Input a map of sea surface temperatures
  • 53.
    Visualizing something wealready 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
  • 54.
    INPUT LAYER HIDDEN LAYERS OUTPUTLAYER 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]
  • 55.
    1960-1999: ANNUAL MEANTEMPERATURE TRENDS Greenhouse gases fixed to 1920 levels [AEROSOLS PREVAIL] Industrial aerosols fixed to 1920 levels [GREENHOUSE GASES PREVAIL] All forcings [STANDARD CESM-LE] DATA
  • 56.
    1960-1999: ANNUAL MEANTEMPERATURE TRENDS Greenhouse gases fixed to 1920 levels [AEROSOLS PREVAIL] Industrial aerosols fixed to 1920 levels [GREENHOUSE GASES PREVAIL] All forcings [STANDARD CESM-LE] DATA
  • 57.
    1960-1999: ANNUAL MEANTEMPERATURE TRENDS Greenhouse gases fixed to 1920 levels [AEROSOLS PREVAIL] Industrial aerosols fixed to 1920 levels [GREENHOUSE GASES PREVAIL] All forcings [STANDARD CESM-LE] DATA
  • 58.
    1960-1999: ANNUAL MEANTEMPERATURE TRENDS Greenhouse gases fixed to 1920 levels [AEROSOLS PREVAIL] Industrial aerosols fixed to 1920 levels [GREENHOUSE GASES PREVAIL] All forcings [STANDARD CESM-LE] DATA
  • 59.
    CLIMATE MODEL DATAPREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 60.
    OBSERVATIONS PREDICT THEYEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 61.
    OBSERVATIONS SLOPES PREDICT THE YEARFROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 62.
    HOW DID THEANN MAKE ITS PREDICTIONS?
  • 63.
    HOW DID THEANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?
  • 64.
    RESULTS FROM LRP [Labeand Barnes 2021, JAMES] Low High
  • 65.
    RESULTS FROM LRP [Labeand Barnes 2021, JAMES] Low High
  • 66.
    RESULTS FROM LRP [Labeand Barnes 2021, JAMES] Low High
  • 67.
    RESULTS FROM LRP [Labeand Barnes 2021, JAMES] Low High
  • 68.
    Higher LRP valuesindicate 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]
  • 69.
    Greenhouse gas-driven Aerosol-driven All forcings AVERAGEDOVER 1960-2039 [Labe and Barnes 2021, JAMES]
  • 70.
    KEY POINTS FROMEXAMPLE #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
  • 73.
  • 74.
  • 75.
  • 76.
  • 77.
  • 78.
  • 79.
  • 80.
  • 81.
    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
  • 82.
    Select one ensemble memberand calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
  • 83.
    Calculate 10-year moving (linear)trends 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
  • 84.
    Plot the slopeof the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY [Labe and Barnes, 2022; GRL]
  • 85.
    Calculate a threshold fordefining a slowdown in decadal warming [Labe and Barnes, 2022; GRL]
  • 86.
    Repeat this exercisefor each ensemble member in CESM2-LE [Labe and Barnes, 2022; GRL]
  • 87.
    Compare warming slowdowns with reanalysis(ERA5) [Labe and Barnes, 2022; GRL]
  • 88.
  • 89.
    OCEAN HEAT CONTENT– 100 M Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]
  • 90.
    OCEAN HEAT CONTENT– 100 M INPUT LAYER Start with anomalous ocean heat… [Labe and Barnes, 2022; GRL]
  • 91.
    OCEAN HEAT CONTENT– 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN Will a slowdown begin? [Labe and Barnes, 2022; GRL]
  • 92.
    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]
  • 93.
    So how welldoes the neural network do? [Labe and Barnes, 2022; GRL]
  • 94.
    Low High ColderWarmer [Labe and Barnes, 2022; GRL]
  • 95.
    Low High ColderWarmer [Labe and Barnes, 2022; GRL]
  • 96.
    Low High ColderWarmer [Labe and Barnes, 2022; GRL]
  • 97.
    What about observations? Future(2012-) so-called “hiatus” Comparing observations to the IPO [Labe and Barnes, 2022; GRL]
  • 98.
    What about observations? Future(2012-) so-called “hiatus” 2021 Looking ahead to the near- future… ?
  • 99.
    What about observations? ColderWarmer [2003, 2004] [2016, 2017] [Labe and Barnes, 2022; GRL]
  • 100.
    KEY POINTS FROMEXAMPLE #2 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
  • 101.
    Earth is alsowarming 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
  • 102.
  • 103.
  • 104.
  • 105.
  • 106.
    [Po-Chedley et al.2022, PNAS] Partial least squares regression with CMIP6 large ensembles (test observations)
  • 107.
    [Po-Chedley et al.2022, PNAS] UNDERSTANDING OUR PREDICTIONS – Patterns of internal variability (e.g., Interdecadal Pacific Oscillation)
  • 108.
    [Po-Chedley et al.2022, PNAS]
  • 109.
  • 110.
    MACHINE LEARNING ISJUST ANOTHER TOOL TO ADD TO OUR WORKFLOW. 1)
  • 111.
    MACHINE LEARNING IS NOLONGER A BLACK BOX. 2)
  • 112.
    WE CAN LEARNNEW SCIENCE FROM EXPLAINABLE AI. 3)
  • 113.
    KEY POINTS 1. Machinelearning 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 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