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Milutinovic, S. ., University of Pennsylvania, Department of Earth and Environmental Science, Philadelphia, USA, svetlana.milutinovic@nersc.no
Bertino, L. ., Nansen Environmental and Remote Sensing Center, Bergen, Norway,

ASSESSMENT AND PROPAGATION OF UNCERTAINTIES IN INPUT TERMS THROUGH AN OCEAN-COLOUR-BASED MODEL OF PRIMARY PRODUCTIVITY

Uncertainties in input terms were propagated through one of the most widely used net primary productivity (NPP) models via a Monte Carlo method. The study was based on monthly averaged global remote sensing observations from 2005. We found that the typical distribution of uncertainty around the model output was lognormal-like. The nominal NPP values in individual grid cells were typically overestimated by 6%, relative to the means of the associated uncertainty distributions. The random component of uncertainty in NPP, expressed as the coefficient of variation, was 108% on average. The positive systematic errors accumulated to an overestimate of 2.5 Pg C in the annual global NPP of 46.1 Pg C. The input quantity that contributed most to the systematic uncertainty in NPP was the parameter representing irradiance-dependent vertical changes in chlorophyll-normalized photosynthetic rates. On the other hand, the largest contributor to the random uncertainty in NPP was the term describing the physiological state of phytoplankton. Thus, reductions in the respective uncertainties in these two input terms could improve the accuracy of the NPP model the most.

Poster presentation

Session #:044
Date: 2/23/2012
Time: 17:00 - 18:00
Location: Poster/Exhibit Hall

Presentation is given by student: Yes

PosterID: A0562