McKinley, Galen: Physical knowledge to improve and extend machine learning pCO2 reconstructions
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ICOS Science Conference 2022: Day 1, Session 6
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McKinley, Galen: Physical knowledge to improve and extend machine learning pCO2 reconstructions
- 1. Physical knowledge to improve and extend machine learning pCO2 reconstructions Galen A. McKinley Valerie Bennington, Lucas Gloege, Amanda Fay Columbia University, Earth and Environmental Science Lamont-Doherty Earth Observatory ICOS Science Conference September 13, 2022
- 2. 2 Uncertainties remain significant for the ocean carbon sink. And observation-based products are temporally limited. Friedlingstein et al., 2022 sink sink Fluxes since 1850 (in GtCO2/yr) Ocean flux since 1960 (in GtCO2/yr)
- 3. 3 Limitations of observation-based reconstructions of pCO2 • Few observations in 1980s and none prior • Limited explain-ability from machine learning • Existing knowledge about CO2 dynamics is often not incorporated into algorithms Gloege et al., JAMES 2021 µatm
- 4. 4 Limitations of observation-based reconstructions of pCO2 • Few observations in 1980s and none prior • Limited explain-ability from machine learning • Existing knowledge about CO2 dynamics is often not incorporated into algorithms Gloege et al., JAMES 2021 µatm
- 5. 5 The impact of temperature on pCO2 is very well established
- 6. 6 Method #1: pCO2-Residual • Sea Surface Temperature has a known direct impact on ocean pCO2 • Biogeochemistry and physical processes drive the remaining variability • By removing the temperature component, we focus the statistics on biogeochemical-physical impacts on pCO2 Takahashi et al., 1993 pCO2-T Bennington et al. 2022, in review Mean pCO2
- 7. 7 Mean pCO2 + pCO2-T (blue) and observed pCO2 (red) pCO2 Residual = (pCO2-pCO2-T) monthly SST
- 8. 8 pCO2-Residual values make physical sense and are approximately normally distributed Bennington et al. 2022, in review
- 9. 9 Reconstruct pCO2-Residual with XGBoost pCO2 – T = pCO2 pCO2 - Residual 1. XGB learns pCO2-Residual as function of features 2. Combine with pCO2-T for final result Input data (“Features”) • Satellite data − Sea Surface Temp. (SST) − Chlorophyll-a (Chl-a) ▪ Monthly climatological − Mixed layer depth − Sea Surface Salinity (SSS) ▪ Location and time − Day of year (DOY) − Latitude, Longitude (n- vector) ▪ xCO2 Bennington et al. 2022, in review
- 10. 10 pCO2-Residual performs well against independent data Bennington et al 2022, in review
- 11. 11 Flux timeseries, 1985-2019 Bennington et al. 2022, in review
- 12. 12 Ocean models have long been used as the basis for the Global Carbon Budget. Can these be used to support reconstruction of real ocean pCO2?
- 13. 13 Method #2: Hybrid Data Physics • Use GCB Hindcast models as a first guess (or “prior”) • Calculate the difference between the model pCO2 and SOCAT data • Apply XGB algorithm to reconstruct a full-field model correction • Get final estimate of real-world pCO2 by adding this estimated correction at each point © 222 Lamont-Doherty Earth Observatory Hindcast ocean biogeochemical models + SOCAT Data Gloege et al. 2022, JAMES
- 14. 14 Full-coverage misfit individually estimated for each model, month Gloege et al. 2022, JAMES pCO2 misfit (uatm)
- 15. 15 Climatological misfits are much larger than interannual Princeton Model, others similar Bennington et al. 2022 GRL
- 16. 16 Since climatological misfit dominates, how much skill is gained by applying only this as correction, as opposed to an interannual? • HPDClimTest applies the 2000-2020 climatology of the model-observation misft 1959 2020 1982 LDEO-HPD = Model pCO2 + Interannual Misfit HPD: Model pCO2 + Climatological Misfit Observations Begin Model Period Begins HPDClimTest = Model pCO2 + Climatological Misfit Bennington et al. 2022 GRL
- 17. 17 Most improvement over original models is climatological Comparison data (1990-2020) not used in algorithm training: GLODAP and LDEO pCO2 (not in SOCAT) Bennington et al. 2022 GRL
- 18. 18 Most improvement over original models is climatological Comparison data (1990-2020) not used in algorithm training: GLODAP and LDEO pCO2 (not in SOCAT) Bennington et al. 2022 GRL
- 19. 19 Most improvement over original models is climatological Comparison data (1990-2020) not used in algorithm training: GLODAP and LDEO pCO2 (not in SOCAT) Bennington et al. 2022 GRL
- 20. 20 Most improvement over original models is climatological Comparison data (1990-2020) not used in algorithm training: GLODAP and LDEO pCO2 (not in SOCAT) Bennington et al. 2022 GRL
- 21. 21 Apply climatological misfit to extend back to 1959 • HPDClimTest applies the 2000-2020 climatology of the model-observation misfit • Since it dominates, apply this in the pre-observed period 1959 2020 1982 LDEO-HPD = Model pCO2 + Interannual Misfit LDEO-HPD = Model pCO2 + Climatological Misfit Observations Begin Model Period Begins HPDClimTest = Model pCO2 + Climatological Misfit Bennington et al. 2022 GRL
- 22. 22 LDEO-HPD: Air-sea CO2 flux, 1959-2020 Bennington et al. 2022 GRL
- 23. 23 Conclusions • Physical knowledge can be incorporated into machine learning algorithms, and leads to improved reconstruction skill • pCO2-Residual • Focuses the statistics on the biogeochemical-physical component of pCO2 • LDEO-HPD • Uses suite of hindcast ocean models as a prior, corrects with SOCAT data • Climatological correction most impactful; supporting extension back to 1959
- 25. 25
- 26. 26 Flux timeseries, 1985-2019 Bennington et al. 2022 in review