epoca wp9: from process studies to ecosystem models participants involved: lov, uib, ifm-geomar,...

EPOCA WP9:EPOCA WP9:From process studies to From process studies to

ecosystem modelsecosystem models

Participants involved:

LOV, UiB, IFM-GEOMAR, GKSS, KNAW,

UGOT, UNIVBRIS

(a.o. J.-P. Gattuso, R. Bellerby, M. Schartau, J. Middelburg, A. Oschlies)

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.

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

Example: calcification & temperatureUVic model: temperature dependence helps to get

latitudinal distribution of rain ratio “right”:

(Schmittner et al., 2008)

Example: calcification & temperature

Does this give meaningful results in global-warming runs?

PICprod

PICprodPP

EP

Increase in PIC production closely linked to temperature-driven increase in Prim.Prod.

(Schmittner et al., 2008)

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

Objectives• Integration & Synthesis

experiments models

Efficient knowledge transfer

Feedback to efficiently reduce uncertainty

Approach1. Analysis

experiments models

Coherent data base(organisms, ecosystems)

Meta-analysis(mesocosm, microcosm)

Meta-analysis(model assumptions,parameterisations)

T9.1

T9.2

T9.3

Approach2. Modelling of micro- and mesocosm experiments

2. Model improvement: balance complexity, performance, portability

3. Assessment and recommendations for incorporation into global-scale modelsexperiments models

Data-assimilative parameter estimation T9.4

T9.5

T9.6

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)

Example 1Calibration by chemostat/turbidostat data

(Pahlow & Oschlies, subm.)

Chain model of N, P, light colimitation

Example 2Calibration by mesocosm data

(Schartau et al., 2007)

Example 3: Transfer to global models

350 ppm700 ppm1050 ppm

(Riebesell et al., 2007) (Oschlies et al., subm.)

50% increasein suboxicvolume(<5mmol/m3)

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?