Forced climate signals with explainable AI and large ensembles
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1) The document discusses using artificial neural networks and explainable AI methods to analyze climate model simulations and detect climate signals related to different external forcings like greenhouse gases and aerosols. 2) A case study examines temperature trends in simulations with varying external forcings, and the neural network is able to more accurately predict observations when trained on a simulation with evolving greenhouse gases. 3) Explainable AI methods reveal the neural network relies more on patterns related to greenhouse gas forcing compared to aerosol or preindustrial forcing patterns when making predictions.
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Forced climate signals with explainable AI and large ensembles
- 1. Forced climate signals with explainable AI and large ensembles @ZLabe Zachary M. Labe Postdoc at Princeton University and NOAA GFDL 10 February 2023 AOS Student/Postdoc Seminar https://zacklabe.com/
- 2. Machine Learning is not new! But…
- 3. Machine Learning is not new!
- 5. Artificial Intelligence Machine Learning Deep Learning Computer Science
- 6. Computer Science Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Labeled data Classification Regression Unlabeled data Clustering Dimension reduction
- 7. • Do it better • e.g., parameterizations in climate models are not perfect, use ML to make them more accurate • Do it faster • e.g., code in climate models is very slow (but we know the right answer) - use ML methods to speed things up • Do something new • e.g., go looking for non-linear relationships you didn’t know were there Very relevant for research: may be slower and worse, but can still learn something WHY SHOULD WE CONSIDER MACHINE LEARNING?
- 8. GROWING DATA
- 10. ADAPTED FROM EYRING ET AL. 2016 CMIP6
- 11. CMIP7??
- 12. GROWING TOOLS
- 14. Python tools for machine learning
- 15. Machine learning for weather IDENTIFYING SEVERE THUNDERSTORMS Molina et al. 2021 Toms et al. 2021 CLASSIFYING PHASE OF MADDEN-JULLIAN OSCILLATION SATELLITE DETECTION Lee et al. 2021 DETECTING TORNADOES McGovern et al. 2019
- 16. Machine learning for climate FINDING FORECASTS OF OPPORTUNITY Mayer and Barnes, 2021 PREDICTING CLIMATE MODES OF VARIABILITY Gordon et al. 2021 TIMING OF CLIMATE CHANGE Barnes et al. 2019
- 17. Machine learning for oceanography CLASSIFYING ARCTIC OCEAN ACIDIFICATION Krasting et al. 2022 TRACK AND REVEAL DEEP WATER MASSES Sonnewald and Lguensat, 2021 ESTIMATING OCEAN SURFACE CURRENTS Sinha and Abernathey, 2021
- 21. X1 X2 INPUTS Artificial Neural Networks [ANN]
- 22. Linear regression! Artificial Neural Networks [ANN] X1 X2 W1 W2 ∑ = X1W1+ X2W2 + b INPUTS NODE
- 23. Artificial Neural Networks [ANN] X1 X2 W1 W2 ∑ INPUTS NODE Linear regression with non-linear mapping by an “activation function” Training of the network is merely determining the weights “w” and bias/offset “b" = factivation(X1W1+ X2W2 + b)
- 24. Artificial Neural Networks [ANN] X1 X2 W1 W2 ∑ INPUTS NODE = factivation(X1W1+ X2W2 + b) ReLU Sigmoid Linear
- 25. X1 X2 ∑ inputs HIDDEN LAYERS X3 ∑ ∑ ∑ OUTPUT = predictions Artificial Neural Networks [ANN] : : :: INPUTS
- 26. Complexity and nonlinearities of the ANN allow it to learn many different pathways of predictable behavior Once trained, you have an array of weights and biases which can be used for prediction on new data INPUT [DATA] PREDICTION Artificial Neural Networks [ANN]
- 27. X Y Our data
- 28. X Y Our data Just an exercise in curve fitting…
- 29. TEMPERATURE
- 30. TEMPERATURE We know some metadata… + What year is it? + Where did it come from?
- 31. We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS] TEMPERATURE
- 32. TEMPERATURE Neural network learns nonlinear combinations of forced climate patterns to identify the year We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS]
- 33. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping [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. 2022] Surface Temperature Map Precipitation Map + TEMPERATURE We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS]
- 34. ----ANN---- 2 Hidden Layers 10 Nodes each Ridge Regularization Early Stopping [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. 2022] We know some metadata… + What year is it? + Where did it come from? [Labe and Barnes, 2022; ESS]
- 35. THE REAL WORLD (Observations) What is the annual mean temperature of Earth?
- 36. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Anomaly is relative to 1951-1980
- 37. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model
- 38. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again
- 39. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) Let’s run a climate model again & again
- 40. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES
- 41. What is the annual mean temperature of Earth? THE REAL WORLD (Observations) CLIMATE MODEL ENSEMBLES Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change)
- 42. What is the annual mean temperature of Earth? Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) But let’s remove climate change…
- 43. What is the annual mean temperature of Earth? Range of ensembles = internal variability (noise) Mean of ensembles = forced response (climate change) After removing the forced response… anomalies/noise!
- 44. 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)
- 45. 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)
- 46. What is the annual mean temperature of Earth?
- 47. 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
- 48. So what? Greenhouse gases = warming Aerosols = ?? (though mostly cooling) What are the relative responses between greenhouse gas and aerosol forcing?
- 49. Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
- 50. INPUT LAYER Surface Temperature Map ARTIFICIAL NEURAL NETWORK (ANN)
- 51. INPUT LAYER HIDDEN LAYERS OUTPUT LAYER Surface Temperature Map “2000-2009” DECADE CLASS “2070-2079” “1920-1929” ARTIFICIAL NEURAL NETWORK (ANN)
- 52. 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)
- 53. 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]
- 54. Visualizing something we already know…
- 55. Neural Network [0] La Niña [1] El Niño [Toms et al. 2020, JAMES] Input a map of sea surface temperatures
- 56. 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
- 57. 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]
- 58. 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
- 59. 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
- 60. 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
- 61. 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
- 62. CLIMATE MODEL DATA PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
- 63. OBSERVATIONS PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
- 64. OBSERVATIONS SLOPES PREDICT THE YEAR FROM MAPS OF TEMPERATURE AEROSOLS PREVAIL GREENHOUSE GASES PREVAIL STANDARD CLIMATE MODEL [Labe and Barnes 2021, JAMES]
- 65. [Labe and Barnes 2021, JAMES] ARE THE RESULTS ROBUST? YES! COMBINATIONS OF TRAINING/TESTING DATA
- 66. HOW DID THE ANN MAKE ITS PREDICTIONS?
- 67. HOW DID THE ANN MAKE ITS PREDICTIONS? WHY IS THERE GREATER SKILL FOR GHG+?
- 68. RESULTS FROM LRP [Labe and Barnes 2021, JAMES] Low High
- 69. 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]
- 70. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
- 71. DISTRIBUTIONS OF LRP AVERAGED OVER 1960-2039 [Labe and Barnes 2021, JAMES]
- 72. KEY POINTS FROM EXAMPLE #1 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
- 76. [Eischeid et al., submitted]
- 77. Temperature anomalies [ °C ] relative to 1981-2010
- 79. TRENDS IN GFDL SPEAR Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 80. TRENDS IN GFDL SPEAR Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 81. TRENDS IN GFDL SPEAR Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 82. SPEAR climatology is too cold!
- 87. Max year predicted in the 1921-1950 baseline for observations Timing of Emergence
- 90. June – August – Timing of Emergence (ToE) for observations
- 91. How is the neural network able to detect the year prior to 1990? Temperature anomalies [ °C ] relative to 1981-2010
- 92. Mean Absolute Error (MAE) for ensemble member predictions over 1921 to 1989
- 93. Mean Absolute Error (MAE) for ensemble member predictions over 1921 to 1989 for different spatial resolutions 50 km resolution 100 km resolution
- 94. Machine Learning Explainability Methods
- 95. Western USA Central USA Eastern USA
- 96. Western USA Central USA Eastern USA
- 97. Western USA Central USA Eastern USA
- 98. What about the warming hole?
- 99. What about the warming hole?
- 101. Natural Forcings
- 103. 1) Is it aerosols?
- 104. Proof Of Concept
- 105. 2) Is it systematic in CMIP6?
- 106. SPEAR_MED (Flux Adjusted) GFDL FLOR (RCP 8.5)
- 107. Machine Learning Explainability Methods
- 109. TRENDS FROM 1921 TO 1950 Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 110. TRENDS IN SNOW? NO! Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 111. TRENDS IN ALBEDO? MAYBE? Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 112. Weird!
- 113. TRENDS IN EVAPORATION? !!! Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 114. TRENDS IN WATER AVAILABILITY? Fully-Coupled [Historical + SSP5-8.5] SPEAR_MED, but NO anthropogenic aerosols SPEAR_MED, but NO anthropogenic forcings
- 116. P-E for the USA
- 117. P-E for the Western USA
- 119. Western USA Central USA Eastern USA
- 120. INFLUENCE OF RESOLUTION 50 km 100 km
- 121. What about where we live?
- 122. KEY POINTS FROM EXAMPLE #2 1. Increasing spatial resolution improves the ability of neural networks to skillfully detect patterns of climate indicators 2. Externally-forced temperature signals have emerged in the observational record during summer across the United States 3. Increases in wintertime snowfall and springtime runoff over the Western United States are linked to the emergence of temperature signals in GFDL SPEAR in the early 20th century Labe, Z.M., N.C. Johnson, and T.L. Delworth (2023), Forced signals in United States summer temperatures revealed by explainable neural networks in climate models and observations, in prep
- 125. KEY POINTS Zachary Labe zachary.labe@noaa.gov @ZLabe 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 Labe, Z.M., N.C. Johnson, and T.L. Delworth (2023), Forced signals in United States summer temperatures revealed by explainable neural networks in climate models and observations, in prep 1. Machine learning is just another tool to consider for our scientific workflow 2. We can use explainable AI (XAI) methods to peer into the black box of machine learning 3. We can learn new science by using XAI methods in conjunction with existing statistical tools