Limitations and challenges in
modeling marine ecosystems in a
changing climate scenario
Pedro Duarte
Norwegian Polar Institute,
Fram Centre, N-9296 Tromsø,
Norway
Predictive Power of Marine Science in a Changing Climate
COST Science & Technology Strategic Event
7-8 April 2014, Sopot, Poland
Topics
• Concepts behind ecosystem models of the
marine environment
• Similarities and differences between marine
ecosystem models: Which variables and
processes really matter?
• Predictive power of ecosystem models.
• Where to go from here?
Concepts behind ecosystem models of the marine
environment
Similarities and differences between
marine ecosystem models…
Large detritus
C, N, Fe, P, Si, CaCO3
Small detritus
C, N, Fe, P
Zooplankton
C, N, Fe, P
Small phytoplankton
C, N, Fe, P, CaCO3, Chl
Nitrate
Diatoms
C, N, Fe, P, Si, Chl
Ammonium
Silicate
Diazotrophs
C, N, Fe, P, Chl
Phosphate
Moore et at. (2002). Deep-sea research II 49: 403-462
Iron
Similarities and differences between marine
ecosystem models: Which variables and processes
really matter?
Fast sinking detritus
Slow sinking detritus
Microzooplankton
Mesozooplankton
Slow sinking and
mineralization
Fast sinking and
mineralization
Non-diatom
phytoplankton
Nitrogen
Diatom
phytoplankton
Iron
Silicate
Popova et al. (2010). Biogeosciences 7: 3569-3591
1/4o resolution global ocean NEMO (Nucleus for European Modelling of the Ocean) model coupled with
the MEDUSA (Model for Ecosystem Dynamics, carbon Utilisation, Sequestration and Acidification)
Similarities and differences between
marine ecosystem models…
SINMOD
Calanus finmarchicus
Calanus glacialis
Fast sinking detritus
Heterotrophic
nanoflagellates
Dissolved
organic
carbon
Bacteria
Nitrate
Ciliates
Slow sinking detritus
Flagellates
Diatoms
Ammonium
Silicate
Slagstad et al (2011). Prog Oceanogr 90: 117-131
Similarities and differences …
Allen et al. Overview of ERSEM. http://imarnet.org/PDF/7%20ERSEM%20-Allen.pdf
Similarities and differences between marine
ecosystem models…
Biogeochemical cycles
Limiting
factors
•
•
•
•
Single
Multiple
External
Traditional NPZ
(Fasham’s model)
and NPZD models
MEDUSA
NORWECOM
Internal
-
Moore’s et al (2002)
ERSEM
«Lower trophic levels» versus «end-to-end» models
Models coupled offline or online
Coastal versus open-ocean models
Models with physical and biochemical processes running over the same spatial
and temporal grid versus those using different grids for different processes…
Predictive power of ecosystem models
“Marine systems models are becoming increasingly
complex and sophisticated, but far too little attention has
been paid to model errors and the extent to which model
outputs actually relate to ecosystem processes”.
“A summary plot of model performance indicates that
model performance deteriorates as we move through the
ecosystem from the physics, to the nutrients and plankton”
Allen et al (2007). Journal of Marine Systems 68: 381–404
Predictive power of ecosystem models
How to measure the predictive power?
• Model verification – checking if equations are
correct and that results respect the conservation of
mass
• Model validation – cheking if model results
consistently reproduce observations
Model validation may range from the simple visual
inspection of predicted and observed results till the
usage of measures of goodness of fit like covariance
analysis, regression analysis, mean square errors and
cost functions.
Predictive power of ecosystem models
• There were systematic efforts to compare and evaluate the many models
applied to european coastal zones, with emphasis on the North Sea
• Radach & Moll (2006) evaluated the performance of several models in
predicting regional distributions, long-term trends, annual cycles and events
– No model performed well for all state variables
– Performance was better for physical than for biological data and for lower
than for higher trophic levels
– Spring blooms were reasonably reproduced
– Interannual variability was not properly reproduced
– It became evident the importance of having a good physical model and a
good definition of the boundary conditions
– There were some limitations on available data for model evaluation
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Allen et al (2007) Journal of Marine Systems 68: 381–404
Arhonditsis et al. (2004). Mar. Ecol. Prog.Ser. 271, 13–26.
Jones (2002). Mar. Biol. 40, 37–141 (An annual review).
Moll & Radach (2003). Prog. Oceanog. 57: 175–217.
Radach & Moll (2006). Mar. Biol. 44, 1–60 (An Annual Review).
Douro estuary
Nash Sutcliffe model efficiency
(E) (Nash and Sutcliffe, 1970) and
Maréchal (2004) for quality level
criteria
Cost function (CF) (OSPAR
Commission, 1998)
Cost function with quality criteria
according to Radach and Moll
(2006)
Excellent
Very good
Good
Poor
> 0.65
0.65-0.5
0.5-0.2
< 0.2
Very good
Good
Reasonable
Poor
2-5
≥5
2-3
≥3
≤1
1-2
Predictive power of ecosystem models
June
Azevedo et al (2010). Water Research 44: 3133 – 3146
Predictive power of ecosystem models
Chlorophyll
Azevedo et al (2014). Ecological Modelling 272: 1-15
Azevedo et al (2010) Water Research 44: 3133 – 3146
Very good
Good
Poor
Azevedo et al (2014) Ecological Modelling 272: 1-15
June, August and October data – model validation
Where to go from here?
• The aquatic modeling community should adopt generally
accepted standards of model performance.
• Methodological steps typically recommended by classic
modeling texts are: sensitivity analysis, calibration, and
validation
• Some objective validation procedures should be defined and
widely accepted by the scientific community
• Data assimilation techniques and operational modeling
should become more common in ecosystem modeling
• There may be some advantage in the diversity of existing
model approaches for they allow some reciprocal testing
• It is important to make simulation code widely available and
“plug & play” so that researchers may hybridize models
Where to go from here?
Some more specific aspects:
• Modeling of multi-nutrient limitation at the primary producer
level
• Modeling of stoichiometry (including Chl : C)
• Incorporation of the microbial loop
• Incorporation of dissolved organic forms
• Reformulation of the zooplankton grazing term to include algal
food-quality effects on assimilation efficiency
• Refinement of the sediment diagenesis processes
• A stronger physiological basis is also important for obtaining
more accurate simulations of biological processes (SFG versus
DEB approaches)
• Introduction of goal functions into sub-models of biological
components
Thank you!