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Explainable AI for identifying regional climate change patterns

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Zachary Labe
Zachary Labe

13 January 2023… Scientific Machine Learning Community (Presentation): Explainable AI for identifying regional climate change patterns, University of Leeds, UK. Remote Presentation.

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Explainable AI for identifying regional climate change patterns @ZLabe Zachary M. Labe Postdoc at Princeton University and NOAA GFDL 13 January 2023 – University of Leeds Scientific Machine Learning Community (SciML) https://zacklabe.com/
• 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?
GROWING DATA
Today’s weather or climate scientist is far more likely to be debugging code written in Python… than to be poring over satellite images or releasing radiosondes. “ D. Irving| Bulletin of the American Meteorological Society| 2016
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
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]
[Labe and Barnes 2021, JAMES] ARE THE RESULTS ROBUST? YES! COMBINATIONS OF TRAINING/TESTING DATA
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]
DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
Earth is warming!
OBSERVATIONS
Adapted from Peings et al. 2018, ERL AA UTW LENS Stratosphere Troposphere 2100-2070 minus 1981-2010 Antarctic Equator Arctic CLIMATE MODEL PROJECTION
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]
Earth is warming!
https://research.noaa.gov/article/ArtMID/587/ArticleID/2756/Simulated- geoengineering-evaluation-cooler-planet-but-with-side-effects
Could we detect whether we were under the influence of stratospheric aerosol injection (SAI) using regional climate patterns?
TEMPERATURE YEAR 2045 SAI? SAI?
PRECIPITATION YEAR 2045 SAI? SAI?
LET’S TRY ANOMALIES YEAR 2045
PROJECTIONS OF TEMPERATURE [Labe et al. 2023, EarthArXiv]
PROJECTIONS OF PRECIPITATION [Labe et al. 2023, EarthArXiv]
CAN WE DETECT A SAI WORLD? LOGISTIC REGRESSION [Labe et al. 2023, EarthArXiv]
CLIMATOLOGICAL MAPS OF ARISE-SAI-1.5 IN 2050-2069 MEAN STATE [Labe et al. 2023, EarthArXiv]
DECADAL TRENDS TEMPERATURE [Labe et al. 2023, EarthArXiv]
DECADAL TRENDS TEMPERATURE [Labe et al. 2023, EarthArXiv]
DECADAL TRENDS PRECIPITATION [Labe et al. 2023, EarthArXiv]
DECADAL TRENDS PRECIPITATION [Labe et al. 2023, EarthArXiv]
N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
CAN WE DETECT A SAI WORLD? [Labe et al. 2023, EarthArXiv]
[Labe et al. 2023, EarthArXiv]
[Labe et al. 2023, EarthArXiv]
Central Africa [Labe et al. 2023, EarthArXiv]
HOW DID THE ML MODEL KNOW? [Labe et al. 2023, EarthArXiv]
…Using regional climate patterns! [Labe et al. 2023, EarthArXiv]
CAN WE DETECT A SAI WORLD? [Labe et al. 2023, EarthArXiv]
N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
TEMPERATURE [Labe et al. 2023, EarthArXiv]
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 add to 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 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., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in a simulation of stratospheric aerosol injection. EarthArXiv, DOI: 10.31223/X5394Z
Explainable AI for identifying regional climate change patterns

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Explainable AI for identifying regional climate change patterns

  • 1. Explainable AI for identifying regional climate change patterns @ZLabe Zachary M. Labe Postdoc at Princeton University and NOAA GFDL 13 January 2023 – University of Leeds Scientific Machine Learning Community (SciML) https://zacklabe.com/
  • 2. • 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?
  • 4.
  • 5. Today’s weather or climate scientist is far more likely to be debugging code written in Python… than to be poring over satellite images or releasing radiosondes. “ D. Irving| Bulletin of the American Meteorological Society| 2016
  • 6. 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
  • 7. 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
  • 8. 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
  • 13. TEMPERATURE We know some metadata… + What year is it? + Where did it come from?
  • 14. We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS] TEMPERATURE
  • 15. 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]
  • 16. ----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]
  • 17. ----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]
  • 18. THE REAL WORLD (Observations) What is the annual mean temperature of Earth?
  • 19. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
  • 20. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model
  • 21. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again
  • 22. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again & again
  • 23. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES
  • 24. 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)
  • 25. 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…
  • 26. 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!
  • 27. 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)
  • 28. 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)
  • 29. What is the annual mean temperature of Earth?
  • 30. 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
  • 31. So what? Greenhouse gases = warming Aerosols = ?? (though mostly cooling) What are the relative responses between greenhouse gas and aerosol forcing?
  • 32. Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  • 33. INPUT LAYER Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  • 34. INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
  • 35. 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)
  • 36. 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]
  • 37. 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
  • 38. 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
  • 39. 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
  • 40. LAYER-WISE RELEVANCE PROPAGATION (LRP) Image Classification LRP https://heatmapping.org/ NOT PERFECT Crock Pot Neural Network Backpropagation – LRP WHY
  • 41. [Adapted from Adebayo et al., 2020] EXPLAINABLE AI IS NOT PERFECT THERE ARE MANY METHODS
  • 42. [Adapted from Adebayo et al., 2020] THERE ARE MANY METHODS EXPLAINABLE AI IS NOT PERFECT
  • 43. Visualizing something we already know…
  • 44. Neural Network [0] La Niña [1] El Niño [Toms et al. 2020, JAMES] Input a map of sea surface temperatures
  • 45. 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
  • 46. 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]
  • 47. 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
  • 48. 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
  • 49. 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
  • 50. 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
  • 51. CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 52. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 53. OBSERVATIONS SLOPES PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
  • 54. [Labe and Barnes 2021, JAMES] ARE THE RESULTS ROBUST? YES! COMBINATIONS OF TRAINING/TESTING DATA
  • 55. HOW DID THE ANN MAKE ITS PREDICTIONS?
  • 56. HOW DID THE ANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?
  • 57. RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
  • 58. RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
  • 59. RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
  • 60. RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
  • 61. 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]
  • 62. Greenhouse gas-driven Aerosol-driven All forcings AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
  • 63. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
  • 64. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
  • 70. [Po-Chedley et al. 2022, PNAS] Partial least squares regression with CMIP6 large ensembles (test observations)
  • 71. [Po-Chedley et al. 2022, PNAS] UNDERSTANDING OUR PREDICTIONS – Patterns of internal variability (e.g., Interdecadal Pacific Oscillation)
  • 72. [Po-Chedley et al. 2022, PNAS]
  • 74.
  • 75.
  • 77. Could we detect whether we were under the influence of stratospheric aerosol injection (SAI) using regional climate patterns?
  • 81.
  • 82.
  • 83. PROJECTIONS OF TEMPERATURE [Labe et al. 2023, EarthArXiv]
  • 84. PROJECTIONS OF PRECIPITATION [Labe et al. 2023, EarthArXiv]
  • 85. CAN WE DETECT A SAI WORLD? LOGISTIC REGRESSION [Labe et al. 2023, EarthArXiv]
  • 86. CLIMATOLOGICAL MAPS OF ARISE-SAI-1.5 IN 2050-2069 MEAN STATE [Labe et al. 2023, EarthArXiv]
  • 87. DECADAL TRENDS TEMPERATURE [Labe et al. 2023, EarthArXiv]
  • 88. DECADAL TRENDS TEMPERATURE [Labe et al. 2023, EarthArXiv]
  • 89. DECADAL TRENDS PRECIPITATION [Labe et al. 2023, EarthArXiv]
  • 90. DECADAL TRENDS PRECIPITATION [Labe et al. 2023, EarthArXiv]
  • 91. N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
  • 92. N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
  • 93. CAN WE DETECT A SAI WORLD? [Labe et al. 2023, EarthArXiv]
  • 94. [Labe et al. 2023, EarthArXiv]
  • 95. [Labe et al. 2023, EarthArXiv]
  • 96. Central Africa [Labe et al. 2023, EarthArXiv]
  • 97. HOW DID THE ML MODEL KNOW? [Labe et al. 2023, EarthArXiv]
  • 98. …Using regional climate patterns! [Labe et al. 2023, EarthArXiv]
  • 99. CAN WE DETECT A SAI WORLD? [Labe et al. 2023, EarthArXiv]
  • 100. N Y HIDDEN LAYERS INPUT LAYER INPUT LAYER SAI WORLD? or or map of near-surface temperature map of near-surface temperature map of total precipitation map of total precipitation Years Since SAI Injection OUTPUT LOGISTIC REGRESSION ARTIFICAL NEURAL NETWORK softmax [Labe et al. 2023, EarthArXiv]
  • 101. TEMPERATURE [Labe et al. 2023, EarthArXiv]
  • 103. MACHINE LEARNING IS JUST ANOTHER TOOL TO ADD TO OUR WORKFLOW. 1)
  • 104. MACHINE LEARNING IS NO LONGER A BLACK BOX. 2)
  • 105. WE CAN LEARN NEW SCIENCE FROM EXPLAINABLE AI. 3)
  • 106. KEY POINTS 1. Machine learning is just another tool to add to 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 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., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in a simulation of stratospheric aerosol injection. EarthArXiv, DOI: 10.31223/X5394Z