Investigation of Potential Predictability of the Baltic Sea Ecosystem

Karin Junker

NMA Summer School 2009

Investigation of Potential Predictability of the Baltic Sea Ecosystem

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

Karin Junker


PredictionThe state of tomorrow is a function of the state of today

Prediction depends on the state of todayneeds some kind of transfer functionshould have a skill better than persistency

x i t t = f [ x i t ] i=1. . n

Karin Junker


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 whateverWhere lie the sensitivities?How good is the prediction? Which Models are feasible?

Karin Junker


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?

Karin Junker


Statistical ModelsStatistical Downscaling for investigation of variability and relationship between variablesAnalysis of POPs to further investigate the space-time variability in the datamaybe GAM/T-GAM, Bayesian Modeling,...?

Karin Junker


Statistical DownscalingIdea: 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 datahere:

local data: ecological datalarge scale data: climatological data (NAO index, SST,

Air Temperature, ...)needs long time series (>20y)

Karin Junker


SD Method (Krönke et al 1998)

all combinations of X and Y are tried outafter high skill and high correlation found ecological plausibility will be testedif plausible, a potential relationship has been found

Karin Junker


Downscaling Method: Stastistical Model

Calculate the covariance matrix of the observationsCalculate 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 predictorvalidate this relationship using either a validation period or crossvalidation

Karin Junker


Skill Factorsthe correlation coefficient r between the observations and estimationsBrier-based score: where

variance of error (estimate - observation)variance of observation

SD: Selection of results

=1− e2 / o

2

e2

o2

Karin Junker


POP Analysis

Linear multivariate techniqueused to analyse space-time variability of time series („waves“ in the observational data)Mostly used to find oscillatory modes in climate dataGood for systems with quasi-oscillatory modes and linear processes to the first approximationidea is to find oscillatory modes in ecological data and to get information also about the spatial variability

Karin Junker


Present statusPreparation 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

Investigation of Potential Predictability of the Baltic Sea Ecosystem

Documents

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