data-driven approach to identifying key regions of change associated with future climate scenarios
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Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). https://ams.confex.com/ams/104ANNUAL/meetingapp.cgi/Paper/431300
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data-driven approach to identifying key regions of change associated with future climate scenarios
- 1. A data-driven approach to identifying key regions of change associated with future climate scenarios https://zacklabe.com/ @ZLabe Zachary M. Labe Postdoc in Seasonal-to-Decadal Variability and Predictability Division NOAA GFDL and Princeton University with… Thomas L. Delworth, NOAA GFDL Nathaniel C. Johnson, NOAA GFDL William F. Cooke, NOAA GFDL 31 January 2024 – 104th AMS Annual Meeting Joint Session, J12A – AIES/CVC/Presidential
- 2. 1) Where do we go from here?
- 3. 1) Where do we go from here? 2) How do we disentangle internal climate variability? Feb/Mar 2016 Sep 2023
- 4. 3) How do we account for regional patterns of change?
- 5. Explainable machine learning can distinguish between regional patterns of time-evolving climate change TAKEAWAY MESSAGE
- 6. What future climate scenario are we following?
- 7. THE REAL WORLD (Observations) Data from Berkeley Earth Surface Temperature 1930 2022 What future climate scenario are we following?
- 8. THE REAL WORLD (Observations) Let’s run a climate model One ensemble member 2022 1930 2050 Data from SPEAR_M ED What future climate scenario are we following?
- 9. THE REAL WORLD (Observations) Let’s run a climate model again! Two ensemble members Data from SPEAR_M ED What future climate scenario are we following?
- 10. THE REAL WORLD (Observations) Let’s run a climate model again & again! Three ensemble members Data from SPEAR_M ED What future climate scenario are we following?
- 11. THE REAL WORLD (Observations) CLIMATE MODEL LARGE ENSEMBLE 30 ensemble members in GFDL SPEAR What future climate scenario are we following?
- 12. What future climate scenario are we following? THE REAL WORLD (Observations) CLIMATE MODEL LARGE ENSEMBLE NOAA GFDL – SPEAR_MED Fully-Coupled (AM4/LM4/MOM6/SIS2) Historical + SSP5-8.5 0.5° land/atmosphere, 1.0° ocean also: LO, HI, HI_25 resolutions https://www.gfdl.noaa.gov/spear/ 30 ensemble members in GFDL SPEAR
- 13. Ensemble members in GFDL SPEAR Maps of a given period in each ensemble Inputs for machine learning
- 14. Historical Forcing – GFDL SPEAR Future Scenarios – GFDL SPEAR Can a neural network learn unique patterns of climate change related to each future emission scenario? 1930 2010 2020 2100
- 15. Train a neural network to predict 5 classes (climate scenarios)
- 16. Yearly Maps of T2M Yearly Maps of T2M Neural Network Classify Climate Scenario Artificial Neural Network Output = 5 Classes Yearly Maps of T2M Neural Network Binary Output Binary Output Step #1 Read in gridded maps of a climate variable from SPEAR simulations
- 17. Yearly Maps of T2M Yearly Maps of T2M Neural Network Classify Climate Scenario Artificial Neural Network Output = 5 Classes Yearly Maps of T2M Neural Network Binary Output Binary Output Step #2 Feed data into an artificial neural network with three hidden layers
- 18. Yearly Maps of T2M Yearly Maps of T2M Neural Network Classify Climate Scenario Artificial Neural Network Output = 5 Classes Yearly Maps of T2M Neural Network Binary Output Binary Output Step #3 Classify which climate scenario (n=5) is associated with each map
- 19. Yearly Maps of T2M Yearly Maps of T2M Neural Network Classify Climate Scenario Artificial Neural Network Output = 5 Classes Yearly Maps of T2M Neural Network Binary Output Binary Output Step #4 “How” à XAI
- 20. Predictions for SPEAR_MED Testing Data Accuracy=92% Nearer to predicted class Further from predicted class
- 21. Predictions for SPEAR_MED Testing Data Accuracy=92% Nearer to predicted class Further from predicted class
- 22. Global Mean Surface Temperature Can we identify changes in future climate impacts after rapid mitigation?
- 23. 30 ensembles for GFDL SPEAR_MED 30 ensembles for GFDL SPEAR_MED Input maps from out-of-sample ensembles into classification network 2020 2030 2100
- 24. 2031 2040 Rapid Mitigation Rapid Mitigation 30 ensembles for GFDL SPEAR_MED 30 ensembles for GFDL SPEAR_MED
- 25. Predictions for the ensemble mean from SSP5-3.4OS SSP5-8.5 SSP2-4.5 2015 2055 2065 2095
- 26. Predictions for the ensemble mean from SSP5-3.4OS SSP5-8.5 SSP2-4.5 2055-2060 rapid mitigation begins 2015 2095
- 27. Are these predictions robust across ensemble members? (n=30) SSP5-3.4OS Transition from SSP5-8.5 to SSP2-4.5 2015 2060 2100
- 28. What climate patterns are associated with these transitions? Transition from SSP5-8.5 to SSP2-4.5 Transition from SSP5-8.5 to SSP2-4.5 Transition from SSP2-4.5 to SSP1-1.9 2031 Rapid mitigation 2040 Rapid mitigation
- 29. Yearly Maps of T2M Yearly Maps of T2M Neural Network Classify Climate Scenario Artificial Neural Network Output = 5 Classes Yearly Maps of T2M Neural Network Binary Output Binary Output Steps #5-6
- 30. XAI composites of years associated with the transition from SSP5-8.5 to SSP2-4.5 (a) approx. 2055-2060 (b) approx. 2040-2045
- 31. Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748- 9326/acc81a Framework can be applied to different geographic regions and climate variables Parallel approach for detecting climate intervention scenarios
- 32. Framework can be applied to different geographic regions and climate variables Parallel approach for detecting climate intervention scenarios Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748- 9326/acc81a
- 33. KEY POINTS 1. A neural network applied to large ensembles can distinguish annual mean maps of climate variables for a range of different climate scenarios 2. Regional patterns are revealed by explainable AI are critical for distinguishing climate scenarios even under similar global mean warming 3. Emission scenario classification for the second half of the 21st century is sensitive to a difference in timing of mitigation by ten years zachary.labe@noaa.gov Wednesday, 31 January 2024 104th American Meteorological Society Annual Meeting Joint Session, J12A – AIES/CVC/Presidential