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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,

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Presentation on theme: "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,"— Presentation transcript:

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 low PP, slow microbial loop low PIC prod. high T high PP, fast microbial loop large PIC prod. irrespective of nutrient supply, export production, grazing… low PIC export large PIC export

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 PP EP Increase in PIC production closely linked to temperature-driven increase in Prim.Prod. (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 CO 2 –… Aim for mechanistic models

7 Objectives Integration & Synthesis experimentsmodels Efficient knowledge transfer Feedback to efficiently reduce uncertainty

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

9 Approach 2.Modelling of micro- and mesocosm experiments 2.Model improvement: balance complexity, performance, portability 3.Assessment and recommendations for incorporation into global-scale models experimentsmodels Data-assimilative parameter estimation T9.4 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 1 Calibration by chemostat/turbidostat data (Pahlow & Oschlies, subm.) Chain model of N, P, light colimitation

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

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

14 Questions from model study & feedback to experimentalists Temperature effects vs. pH effects? Observational evidence of pCO 2 -sensitive C:N ratios in the ocean? What is the mechanism for export of excess C?


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