Revealing climate change signals with explainable AI

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1. The document discusses using explainable artificial intelligence methods to analyze climate model data and reveal patterns of climate change. 2. An artificial neural network was trained to predict decades from maps of surface temperature and showed greater skill at predicting decades when greenhouse gases were the only forcing compared to when aerosols were the only forcing. 3. Explainable AI techniques showed the North Atlantic region was particularly important for the neural network's predictions in climate model experiments forced by both aerosols and greenhouse gases.

Science

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)
Range of ensembles
= natural variability (noise)
Mean of ensembles
= forced response (climate change)
CLIMATE MODEL
ENSEMBLES

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?

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
Surface Temperature Map
ARTIFICIAL NEURAL NETWORK (ANN)
Collection of nodes (neurons)
that adjust their weights and
biases across layers in order to
learn signals for making
predictions
Learns nonlinear processes
through selected parameters
in the model

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, in revision]

OUTPUT LAYER
Layer-wise Relevance Propagation
“2000-2009”
DECADE CLASS
“2070-2079”
“1920-1929”
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
WHY?
= LRP HEAT MAPS
[Labe and Barnes 2021, in revision]

Layer-wise Relevance Propagation
BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI
[Labe and Barnes 2021, in revision]
WHY?
= LRP HEAT MAPS
Find regions of “relevance”
that contribute to the
neural network’s
decision-making process

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
[Labe and Barnes 2021, in revision]
AEROSOLS
PREVAIL
GREENHOUSE GASES
PREVAIL
STANDARD
CLIMATE MODEL

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

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

HOW DID THE ANN
MAKE ITS
PREDICTIONS?
WHY IS THERE
GREATER SKILL
FOR GHG+?

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

[Labe and Barnes 2021, in revision]
Greenhouse gas-driven
Aerosol-driven
All forcings
AVERAGED OVER 1960-2039

KEY POINTS
Zachary Labe
zmlabe@rams.colostate.edu
@ZLabe
1. Using explainable AI methods with artificial neural networks (ANNs) reveals patterns of climate
change in climate models
2. ANN trained using a large ensemble simulation without time-evolving aerosols makes
predictions that have a higher correlation with observations
3. The North Atlantic is an important region for the ANN to make predictions in climate model
experiments forced by aerosols and greenhouse gases

The relative roles of individual forcings on large-scale climate variability remain difficult to disentangle within fully-coupled global climate model simulations. Here, we train an artificial neural network (ANN) to classify the climate forcings of a new set of CESM1 initial-condition large ensembles that are forced by different combinations of aerosol (industrial and biomass burning), greenhouse gas, and land-use/land-cover forcings. As a result of learning the regional responses of internal variability to the different external forcings, the ANN is able to successfully classify the dominant forcing for each model simulation. Using recently developed explainable AI methods, such as layerwise relevance propagation, we then compare the patterns of climate variability identified by the ANN between different external climate forcings that are learned by the neural network. Further, we apply this ANN architecture on additional climate simulations from the multi-model large ensemble archive, which include all anthropogenic and natural radiative forcings. From this collection of initial-condition ensembles, the ANN is also able to detect changes in atmospheric internal variability between the 20th and 21st centuries by training on climate fields after the mean forced signal has already been removed. This ANN framework and its associated visualization tools provide a novel approach to extract complex patterns of observable and projected climate variability and trends in Earth system models. (from https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/379553)

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Professor Brian Hoskins discusses the risks of climate change from increasing greenhouse gases in the atmosphere. By emitting these gases, humanity is performing a "very dangerous experiment" with the planet. The climate is already warming, with multiple lines of evidence showing impacts. Models project further warming this century of 2-4°C depending on emissions. Deep cuts in emissions are needed globally by 2050 to limit risks. The UK has a target of an 80% reduction in emissions by 2050, but meeting targets will be challenging and require changes across society.

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Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.

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Revealing climate change signals with explainable AI

1. REVEALING CLIMATE CHANGE SIGNALS WITH EXPLAINABLE AI @ZLabe Zachary M. Labe with Elizabeth A. Barnes Department of Atmospheric Science 30 March 2021 Spring Postdoctoral Research Symposium CSU PASS

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

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

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

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

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

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

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

9. 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)

10. 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)

11. What is the annual mean temperature of Earth?

12. 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

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

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

18. 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)

19. 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, in revision]

20. OUTPUT LAYER Layer-wise Relevance Propagation “2000-2009” DECADE CLASS “2070-2079” “1920-1929” BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI WHY? = LRP HEAT MAPS [Labe and Barnes 2021, in revision]

21. Layer-wise Relevance Propagation BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI [Labe and Barnes 2021, in revision] WHY? = LRP HEAT MAPS Find regions of “relevance” that contribute to the neural network’s decision-making process

22. 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

23. 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

24. 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

25. 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

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

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

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

29. HOW DID THE ANN MAKE ITS PREDICTIONS?

30. HOW DID THE ANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?

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

32. [Labe and Barnes 2021, in revision] Greenhouse gas-driven Aerosol-driven All forcings AVERAGED OVER 1960-2039

33. KEY POINTS Zachary Labe zmlabe@rams.colostate.edu @ZLabe 1. Using explainable AI methods with artificial neural networks (ANNs) reveals patterns of climate change in climate models 2. ANN trained using a large ensemble simulation without time-evolving aerosols makes predictions that have a higher correlation with observations 3. The North Atlantic is an important region for the ANN to make predictions in climate model experiments forced by aerosols and greenhouse gases

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