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Using neural networks to predict temporary slowdowns in decadal climate warming trends

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Using neural networks to predict temporary slowdowns in decadal climate warming trends

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27th Annual CESM Workshop - Earth System Prediction Working Group (ESPWG)

To explore the predictability of temporary slowdowns in future climate warming, we apply an artificial neural network (ANN) to data from CESM2-LE and observations. Here, an ANN is tasked with predicting the onset of a slowdown in the rate of the global mean surface temperature trend by using maps of upper ocean heat content anomalies. Through a machine learning explainability method, we identify key regional patterns the ANN is learning to make its slowdown predictions.

27th Annual CESM Workshop - Earth System Prediction Working Group (ESPWG)

To explore the predictability of temporary slowdowns in future climate warming, we apply an artificial neural network (ANN) to data from CESM2-LE and observations. Here, an ANN is tasked with predicting the onset of a slowdown in the rate of the global mean surface temperature trend by using maps of upper ocean heat content anomalies. Through a machine learning explainability method, we identify key regional patterns the ANN is learning to make its slowdown predictions.

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Using neural networks to predict temporary slowdowns in decadal climate warming trends

  1. 1. USING NEURAL NETWORKS TO PREDICT TEMPORARY SLOWDOWNS IN DECADAL CLIMATE WARMING TRENDS @ZLabe Zachary M. Labe1 with Elizabeth A. Barnes2 1NOAA GFDL and Princeton University; Atmospheric and Oceanic Sciences 2 Colorado State University; Department of Atmospheric Science 16 June 2022 27th Annual CESM Workshop Earth System Prediction Working Group (ESPWG)
  2. 2. Global Warming Hiatus? …in research
  3. 3. Global Warming Hiatus? …in research
  4. 4. Global Warming Hiatus? …in research
  5. 5. Global Warming Hiatus? …in research
  6. 6. Global Warming Hiatus? …in research
  7. 7. Global Warming Hiatus? …in research
  8. 8. Global Warming Hiatus? …in the media, etc.
  9. 9. Global Warming Hiatus? >300 papers, to-date
  10. 10. Global Warming Hiatus?
  11. 11. 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
  12. 12. Select one ensemble member and calculate the annual mean global mean surface temperature (GMST) 2-m TEMPERATURE ANOMALY
  13. 13. Calculate 10-year moving (linear) trends 2-m TEMPERATURE ANOMALY
  14. 14. Plot the slope of the linear trends START OF 10-YEAR TEMPERATURE TREND 2-m TEMPERATURE ANOMALY
  15. 15. Calculate a threshold for defining a slowdown in decadal warming
  16. 16. Repeat this exercise for each ensemble member in CESM2-LE
  17. 17. Compare warming slowdowns with reanalysis (ERA5)
  18. 18. INPUT [DATA] PREDICTION Machine Learning
  19. 19. INPUT [DATA] PREDICTION Machine Learning
  20. 20. INPUT [DATA] PREDICTION Machine Learning Explainable AI Learn new science!
  21. 21. OCEAN HEAT CONTENT – 100 M Start with anomalous ocean heat…
  22. 22. OCEAN HEAT CONTENT – 100 M INPUT LAYER Start with anomalous ocean heat…
  23. 23. OCEAN HEAT CONTENT – 100 M INPUT LAYER HIDDEN LAYERS OUTPUT LAYER YES SLOWDOWN NO SLOWDOWN Will a slowdown begin?
  24. 24. 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?
  25. 25. Visualizing something we already know…
  26. 26. Neural Network [0] La Niña [1] El Niño [Toms et al. 2020, JAMES] Input a map of sea surface temperature
  27. 27. 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
  28. 28. [Adapted from Adebayo et al., 2020] EXPLAINABLE AI IS NOT PERFECT THERE ARE MANY METHODS
  29. 29. [Adapted from Adebayo et al., 2020] THERE ARE MANY METHODS EXPLAINABLE AI IS NOT PERFECT
  30. 30. 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?
  31. 31. So how well does the neural network do?
  32. 32. Low High Colder Warmer
  33. 33. Low High Colder Warmer
  34. 34. Low High Colder Warmer
  35. 35. What about observations? Future (2012-) so-called “hiatus” Comparing observations to the IPO
  36. 36. What about observations? Future (2012-) so-called “hiatus” 2021 Looking ahead to the near- future… ?
  37. 37. What about observations? Colder Warmer [2003, 2004] [2016, 2017]
  38. 38. KEY POINTS 1. An artificial neural network predicts the onset of slowdowns in decadal warming trends of global mean surface temperature 2. Explainable AI reveals the neural network is leveraging tropical patterns of ocean heat content anomalies to make its predictions 3. Transitions in the phase of the Interdecadal Pacific Oscillation are frequently associated with warming slowdown trends in CESM2-LE Zachary Labe zachary.labe@noaa.gov @ZLabe 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

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