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Revealing climate change signals with explainable AI

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Revealing climate change signals with explainable AI

  1. 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. 2. THE REAL WORLD (Observations) What is the annual mean temperature of Earth?
  3. 3. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
  4. 4. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model
  5. 5. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again
  6. 6. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again & again
  7. 7. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES
  8. 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. 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. 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. 11. What is the annual mean temperature of Earth?
  12. 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. 13. So what? Greenhouse gases = warming Aerosols = ?? (though mostly cooling) What are the relative responses between greenhouse gas and aerosol forcing?
  14. 14. Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  15. 15. INPUT LAYER Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  16. 16. 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
  17. 17. INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
  18. 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. 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. 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. 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. 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. 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. 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. 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. 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. 27. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE [Labe and Barnes 2021, in revision] AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL
  28. 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. 29. HOW DID THE ANN MAKE ITS PREDICTIONS?
  30. 30. HOW DID THE ANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?
  31. 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. 32. [Labe and Barnes 2021, in revision] Greenhouse gas-driven Aerosol-driven All forcings AVERAGED OVER 1960-2039
  33. 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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