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Mechanistic Modeling of Calcification and Temperature Effects in Ecosystems

This project aims to develop mechanistic models integrating data and experiments to enhance understanding and prediction of calcification and temperature effects in ecosystems. From analyzing experiments to improving model formulations, the goal is to reduce uncertainty and provide valuable insights for global-scale models. Deliverables include recommendations for model incorporation, data analysis, and uncertainty analysis.

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Mechanistic Modeling of Calcification and Temperature Effects in Ecosystems

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  1. EPOCA WP9:From process studies to ecosystem models Participants involved: LOV, UiB, IFM-GEOMAR, GKSS, KNAW, UGOT, UNIVBRIS (a.o. J.-P. Gattuso, R. Bellerby, M. Schartau, J. Middelburg, A. Oschlies)

  2. Motivation: Current parameterisations of calcification • PIC prod. ~ Prim.Prod. (of some PFT, possibly modulated by ) • PIC prod. ~ Detritus prod. • Essentially all current parameterisations employ Eppley’s temperature dependence.

  3. Calcification & temperature(according to current models) low T high T low PP, slow microbial loop high PP, fast microbial loop low PIC prod. large PIC prod. low PIC export large PIC export irrespective of nutrient supply, export production, grazing…

  4. Example: calcification & temperature UVic model: temperature dependence helps to get latitudinal distribution of rain ratio “right”: (Schmittner et al., 2008)

  5. Example: calcification & temperature Does this give meaningful results in global-warming runs? PICprod PICprod Increase in PIC production closely linked to temperature-driven increase in Prim.Prod. PP EP (Schmittner et al., 2008)

  6. General problem with empirical models • May work well under empirical conditions • No guarantee that this will continue under new environmental conditions • higher temperatures • higher CO2 • … Aim for mechanistic models

  7. Objectives • Integration & Synthesis Efficient knowledge transfer experiments models Feedback to efficiently reduce uncertainty

  8. Approach • Analysis experiments models Coherent data base (organisms, ecosystems) Meta-analysis (model assumptions, parameterisations) T9.1 T9.3 Meta-analysis (mesocosm, microcosm) T9.2

  9. Approach • Modelling of micro- and mesocosm experiments • Model improvement: balance complexity, performance, portability • Assessment and recommendations for incorporation into global-scale models experiments models T9.4 Data-assimilative parameter estimation T9.5 T9.6

  10. Deliverables • D9.1: advice/guidance: data storage/documentation/protocol (month 2, R, PU) • D9.2: structured data base (month 12, R, PP) • D9.3: Mesocosm meta-analysis, guidance to future experiments (month 12, R, PP) • D9.4: Identification of physiological/ecological processes that contribute most to uncertainties in ecosystem models (month 24, R, PU) • D9.5: Improved model formulation for pH-sensitive processes -> Earth system models (month 40, R, PU) • D9.6: Uncertainty analysis (month 48, R, PU)

  11. Example 1Calibration by chemostat/turbidostat data Chain model of N, P, light colimitation (Pahlow & Oschlies, subm.)

  12. Example 2Calibration by mesocosm data (Schartau et al., 2007)

  13. Example 3: Transfer to global models 50% increase in suboxic volume (<5mmol/m3) 350 ppm 700 ppm 1050 ppm (Riebesell et al., 2007) (Oschlies et al., subm.)

  14. Questions from model study & feedback to experimentalists • Temperature effects vs. pH effects? • Observational evidence of pCO2-sensitive C:N ratios in the ocean? • What is the mechanism for export of excess C?

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