Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainable Neural Networks

1. CLIMATE SIGNALS IN CESM1 SINGLE-FORCING LARGE ENSEMBLES REVEALED BY EXPLAINABLE NEURAL NETWORKS @ZLabe Zachary M. Labe with Dr. Elizabeth A. Barnes Department of Atmospheric Science 17 June 2021 Machine Learning: CESM-Related Efforts Cross Working Group 26th Annual CESM Workshop

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) CESM1 LARGE ENSEMBLE

5. 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) CESM1 LARGE ENSEMBLE

6. 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)

7. 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)

8. What is the annual mean temperature of Earth?

9. 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

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

11. Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)

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

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

14. 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)

15. 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]

16. LAYER-WISE RELEVANCE PROPAGATION (LRP) Volcano Great White Shark Timber Wolf Image Classification LRP https://heatmapping.org/ [Geoscience examples in Toms et al. 2020, JAMES] 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

17. LAYER-WISE RELEVANCE PROPAGATION (LRP) Volcano Great White Shark Timber Wolf Image Classification LRP https://heatmapping.org/ [Geoscience examples in Toms et al. 2020, JAMES] 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

18. LAYER-WISE RELEVANCE PROPAGATION (LRP) Volcano Great White Shark Timber Wolf Image Classification LRP https://heatmapping.org/ [Geoscience examples in Toms et al. 2020, JAMES] 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

19. LAYER-WISE RELEVANCE PROPAGATION (LRP) Image Classification LRP https://heatmapping.org/ [Geoscience examples in Toms et al. 2020, JAMES] NOT PERFECT Crock Pot Neural Network Backpropagation – LRP WHY

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, JAMES]

21. Layer-wise Relevance Propagation BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI WHY? = LRP HEAT MAPS Machine Learning Black Box [Labe and Barnes 2021, JAMES]

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

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

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

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

29. [Labe and Barnes 2021, JAMES] ARE THE RESULTS ROBUST? YES! COMBINATIONS OF TRAINING/TESTING DATA

30. HOW DID THE ANN MAKE ITS PREDICTIONS?

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

32. [Labe and Barnes 2021, JAMES]

33. 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]

34. Greenhouse gas-driven Aerosol-driven All forcings AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]

35. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]

36. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]

37. KEY POINTS Zachary Labe zmlabe@rams.colostate.edu @ZLabe 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

38. QUESTIONS Zachary Labe 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

Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainable Neural Networks

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