Successfully reported this slideshow.
Your SlideShare is downloading. ×

Exploring climate model large ensembles with explainable neural networks

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad

Check these out next

1 of 24 Ad
Advertisement

More Related Content

Slideshows for you (19)

More from Zachary Labe (19)

Advertisement

Exploring climate model large ensembles with explainable neural networks

  1. 1. EXPLORING CLIMATE MODEL LARGE ENSEMBLES WITH EXPLAINABLE NEURAL NETWORKS @ZLabe Zachary M. Labe with Elizabeth A. Barnes Colorado State University Department of Atmospheric Science 22 September 2021 World Climate Research Programme (WCRP) Workshop on “Attribution of multi-annual to decadal changes in the climate system”
  2. 2. TEMPERATURE
  3. 3. TEMPERATURE We know some metadata… + What year is it? + Where did it come from?
  4. 4. We know some metadata… + What year is it? + Where did it come from? TEMPERATURE
  5. 5. We know some metadata… + What year is it? + Where did it come from? TEMPERATURE Neural network learns nonlinear combinations of forced climate patterns to identify the year
  6. 6. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping We know some metadata… + What year is it? + Where did it come from? [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. in prep] Surface Temperature Map Precipitation Map + TEMPERATURE
  7. 7. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping We know some metadata… + What year is it? + Where did it come from? [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. in prep]
  8. 8. Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  9. 9. INPUT LAYER Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
  10. 10. INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
  11. 11. 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)
  12. 12. INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Layer-wise Relevance Propagation (LRP) Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI ARTIFICIAL NEURAL NETWORK (ANN) [Labe and Barnes 2021, JAMES]
  13. 13. 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]
  14. 14. Layer-wise Relevance Propagation BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI WHY? = LRP HEAT MAPS Machine Learning Black Box [Labe and Barnes 2021, JAMES]
  15. 15. Layer-wise Relevance Propagation BACK-PROPAGATE THROUGH NETWORK = EXPLAINABLE AI WHY? = LRP HEAT MAPS Find regions of “relevance” that contribute to the neural network’s decision-making process [Labe and Barnes 2021, JAMES]
  16. 16. CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CESM1-LE [Labe and Barnes 2021, JAMES]
  17. 17. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CESM1-LE [Labe and Barnes 2021, JAMES]
  18. 18. OBSERVATIONS SLOPES PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CESM1-LE [Labe and Barnes 2021, JAMES]
  19. 19. Higher LRP values indicate greater relevance for the ANN’s prediction Aerosol-driven Greenhouse gas-driven All forcings Low High [Labe and Barnes 2021, JAMES]
  20. 20. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping TEMPERATURE We know some metadata… + What year is it? + Where did it come from?
  21. 21. TEMPERATURE We know some metadata… + What year is it? + Where did it come from? Train on data from the Multi-Model Large Ensemble Archive
  22. 22. TEMPERATURE We know some metadata… + What year is it? + Where did it come from? NEURAL NETWORK CLASSIFICATION TASK HIDDEN LAYERS INPUT LAYER [Labe and Barnes, in prep]
  23. 23. COMPARING CLIMATE MODELS LRP (Explainable AI) Raw data (Difference from multi-model mean) [Labe and Barnes, in prep]
  24. 24. KEY POINTS Zachary Labe zmlabe@rams.colostate.edu @ZLabe 1. We can learn new climate science by using explainable AI methods in conjunction with existing statistical tools 2. Explainable neural networks reveal patterns of climate change in large ensembles simulated with different combinations of external forcing 3. Neural networks can be used to identify unique model differences and biases between large ensemble simulations and 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

×