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What does a quantitative and “intelligent” model-data comparison mean?

What does a quantitative and “intelligent” model-data comparison mean?. André Paul, Stefan Mulitza and Michal Kucera. “Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons.”.

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What does a quantitative and “intelligent” model-data comparison mean?

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  1. What does a quantitative and “intelligent” model-data comparison mean? André Paul, Stefan Mulitza and Michal Kucera

  2. “Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons.”

  3. “Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons.” Joel Guiot et al. (1999)

  4. Paleoclimate data is generally • sparse • irregularly spaced • characterized by different spatial scales • Should aim for model-data comparison without interpolation to common grid

  5. Example • Sensitivity of simulated (present-day) sea-surface δ13CDIC distribution to air-sea gas exchange formulation Implementing carbon and oxygen isotopes in the MITgcm in collaboration with: Stephanie Dutkiewicz, Jake Gebbie, Annegret Krandick, Takasumi Kurahashi-Nakamura, Martin Losch, Olivier Marchal, Stefan Mulitza, Thejna Tharammal, Rike Völpel

  6. Air-sea gas exchange formulation • Wanninkhoff (1992): quadratic • Krakauer et al. (2006): square-root

  7. Simulated δ13CDIC compared to observations Surface values for 1990, Krakauer et al. (2006) gas exchange Data from Gruber and Keeling (2001), mean ~1.6 permil subtracted Model mean ~1.6 permil subtracted

  8. Simulated δ13CDIC compared to observations Surface values for 1990-2003, Krakauer et al. (2006) gas exchange Data from Schmittner et al. (2013), mean ~1.5 permil subtracted Model mean ~1.5 permil subtracted

  9. http://www.marum.de/compare2012.html PMIP-MARUM-PAGES Workshop “Comparing Ocean Models with Paleo-Archives” (Bremen, Germany, 18–22 March 2012) - participants: Ballarotta M, Braconnot P, Brady E, Karami P, Chen MT, Crucifix M, Fischer N, Dail H, de Garidel-Thoron T, Gebbie J, Groeneveld J, Harrison S, Hertzberg J, Jungclaus J, Kageyama M, Kucera M, Kurahashi T, Laepple T, Lea D, Mariotti V, Merkel U, Milker Y, Mix A, Mulitza S, Paul A, Prange M, Rosell-Melé A, Roy T, Schneider B, Shumilovskikh L, Tharammal T, Waelbroek C, Zhang X  PAGES News 20(2):102, 2012

  10. Conclusion of COMPARE workshop: • “Future model-data comparisons need to be quantitative and ‘intelligent’ (that is, diagnostic and process-oriented)”

  11. How to quantify model-data misfit? • How to quantify model error? • What “metrics” can paleo-ocean data provide?

  12. How to quantify model-data misfit? • Data assimilation: minimize single measure of model-data misfit (“cost”)

  13. How to quantify model-data misfit? • Benchmarking: allows for multiple “metrics” • More than one/less than 20 may be useful

  14. How to quantify model-data misfit?

  15. How to quantify model-data misfit?

  16. How to quantify model-data misfit? • Beyond point-by-point: comparing similarity of patterns even if shifted

  17. How to quantify model-data misfit? Model Data Calculating the Hagaman distance between two maps (Guiot et al., 1999, Fig. 5) left-right fuzzy number left-right fuzzy number

  18. How to quantify model error? • Model error, structural error or bias: due to errors in “physics” or parameterizations • Identify discrepancy that remains after optimizing model parameters ( δ13CDIC bias in South Atlantic Ocean) • In theory perfect observations are required

  19. What “metrics” can paleo-ocean data provide? Hagaman distance (Guiot et al., 1999) and normalized mean-square error (P. Gleckler et al., 2008) just for annual MARGO (2009) SST IPCC WG1 AR5, Fig. 9.12

  20. What “metrics” can paleo-ocean data provide? Bottom-water temperature and salinity at just two ODP sites IPCC WG1 AR5, Fig. 9.18

  21. What “metrics” can paleo-ocean data provide? • Further temperature metrics: tropical cooling and east-west gradients Map of gridded (5°x5°) MARGO (2009) LGM SST annual mean anomalies for the 30° S-30° N tropical band, including location of data points

  22. What “metrics” can paleo-ocean data provide? • Property and density gradients: • Glacial North Atlantic chemocline (in terms of benthic δ13CDIC and δ18Oc) • Density gradients that drive AMOC • Atlantic north-south density gradient ρAtl between southern tip of Africa and regions of deep water formation (Schmittner et al., 2002) • Density contrast ρSNbetween AABW and NADW (e.g. Weber et al., 2008) • 231Pa/230Th (Lippold et al., 2012)

  23. Conclusions • Design metrics that • go beyond point-by-point comparison • test for entire processes • Include quantities other than temperature • E.g., sea ice, density, nutrient or productivity proxies • Average paleo-ocean data over regions or water masses • Extend model-data distance calculation to variable horizontal and vertical resolution • Develop easy-to-use software package

  24. Conclusions • Many “metrics” are available that may be adapted to paleoclimate (and in particular paleocean) model-data comparions. • Paleo-metrics seem indeed to be conclusive in spite of uncertainties.

  25. Implementing carbon isotopes MITgcm carbon cycle component (“DIC package”): atmosphere gas exchange production of organic carbon and calcium carbonate Organic production and remineralization at every depth level remineralization Cf. Dutkiewicz et al. (2005)

  26. Additional state variables DO13C, DO14C, DI13C, DI14C, p13CO2and p14CO2 • Fractionation of carbon isotopes during photosynthesis and air-sea gas exchange • Closed carbon cycle • using “real freshwater flux” boundary condition, nonlinear free surface and balanced precipitation and evaporation (no “virtual fluxes” or “salinity restoring”) Cf. Marchal et al. (1998), Schulz (1998)

  27. Fractionation during air-sea gas exchange: Zhang et al. (1995) where: Furthermore, and • Depends on water temperature and carbonate chemistry ([CO32-]) at surface

  28. Fractionation during photosynthesis: • Depends on water temperature and carbonate chemistry ([CO32-], [CO2(aq)]) in photic zone Jasper et al. (1994), Zhang et al. (1995)

  29. Fractionation during photosynthesis:

  30. Testing sensitivity to air-sea gas exchange formulation • Wanninkhoff (1992): • Krakauer et al. (2006):

  31. Preliminary results • Pre-industrial equilibrium experiments forced by atmospheric concentrations: • p13CO2 = 278 μatm • δ13Catm = -6.5 permil • δ13Catm = 0 permil

  32. Outlook (1) • Study sensitivity to • wind-speed field (SSM/I vs. ECMWF) • maximum value of organic production • fractionation coefficients • … • Your suggestion(s)?

  33. Implementing water isotopes • Added state variables (mass ratios) RH216O, RH218O, RHD16O • Isotopic content of precipitation and water vapor obtained from NCAR Community Atmospheric Model with isotopes (IsoCAM) • Kinetic fractionation during evaporation treated explicitly in ocean model

  34. Data from NASA GISS Global Seawater Oxygen-18 Database Model results by Annegret Krandick

  35. Outlook (2) • Force with LGM boundary conditions • From NCAR IsoCAM (by Thejna Tharammal, David Noone) • Compare to LGM δ18Oc(e.g., by the MARGO project)

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