Investigation of Potential Predictability of the Baltic Sea Ecosystem

1. Biological Department Leibniz-Institute for Baltic Sea Research Warnemünde AMBER Project Research Cluster A (Time Series Analysis) WP 3 for more information: www.io-warnemuende.de/amber.html Supervisor: Joachim Dippner Investigation of Potential Predictability of the Baltic Sea Ecosystem

2. Prediction • The state of tomorrow is a function of the state of today • Prediction depends on the state of today • needs some kind of transfer function • should have a skill better than persistency

3. Questions • Do we know the actual state? • Do we know the dominating processes? • Can we define a transfer function? – be it a differential operator, some predictor filter or whatever • Where lie the sensitivities? • How good is the prediction? • Which Models are feasible?

4. Follow ups • How will the ecosystem change under anthropogenic induced changes (climate, land use, fishing) • Is it possible to identify early indicators, thresholds, quality objectives?

5. Statistical Models • Statistical Downscaling for investigation of variability and relationship between variables • Analysis of POPs to further investigate the space-time variability in the data • maybe GAM/T-GAM, Bayesian Modeling,...?

6. Statistical Downscaling • Idea: to find a relationship between observed large scale data and local data and using this empirical model to estimate local data from modeled large scale data • here: • local data: ecological data • large scale data: climatological data (NAO index, SST, Air Temperature, ...) • needs long time series (>20y)

7. SD Method (Krönke et al 1998) • all combinations of X and Y are tried out • after high skill and high correlation found ecological plausibility will be tested • if plausible, a potential relationship has been found

8. Downscaling Method: Stastistical Model • Calculate the covariance matrix of the observations • Calculate EOFs (=PCA) for the data vectors of interest • reduces dimensionality • reduces noise • Do a CCA on the time coefficients (loadings) to find the relationship between the predicand and predictor • validate this relationship using either a validation period or crossvalidation

9. SD: Selection of results • Skill Factors • the correlation coefficient r between the observations and estimations • Brier-based score: where variance of error (estimate - observation) variance of observation

10. POP Analysis • Linear multivariate technique • used to analyse space-time variability of time series („waves“ in the observational data) • Mostly used to find oscillatory modes in climate data • Good for systems with quasi-oscillatory modes and linear processes to the first approximation • idea is to find oscillatory modes in ecological data and to get information also about the spatial variability

11. Present status • Preparation and investigation of time series of the Mecklenburg-Vorpommern monitoring programme: • time series of physical, chemical and biological data in some cases starting 1970 • ca 200 phytoplankton species • stations lie off the coast of Mecklenburg- Vorpommern • and for recreation: recoding the POP-Analysis program to run on PCs with open source libraries