Effects of sparse sampling on determining long-term trend of global chlorophyll
Supervisor: Stephanie Henson (National Oceanography Centre Southampton, University of Southampton)
Over the past decades marine phytoplankton has been studied due to its high importance for marine primary production as well as its effects on climate and biogeochemical cycles. Numerous studies have been carried out in order to detect the long-term trends based on satellite datasets, in situ measurements or model outputs of chlorophyll and phytoplankton (Boyce et al., 2014; Boyce et al., 2010; Gregg et al., 2005), usually presenting ambiguous results. Limitations on the long‐term trend detection can be due to data scarcity, their patchy nature (Beaulieu et al., 2013) or different collection methods. This project examines how these limitations of in situ sampling affect our ability to reliably determine long‐term trends in chlorophyll concentration. A global biogeochemical model (GFDL’s TOPAZ model; Dunne et al., 2012) was subsampled at the same times and locations as a large database of in situ chlorophyll measurements assembled by Boyce et al. (2012). Trends calculated from in situ measurements and subsampled model output are compared to trends calculated from the full model to determine the influence of sparse sampling on trend detection. Similar methodology as Boyce et al. (2010) has been followed (Generalised Additive Models). Statistical analyses, including Regression analysis, were applied to the data in order to analyze the biogeochemical model output as well as to find its similarities to in situ chlorophyll measurements. Conclusively, a general reduction of mean chlorophyll rates in all regions studied has been observed, coming into agreement with Boyce et al. (2010). Additionally, the comparison of model fits on different datasets (in situ, subsampled model and full model outputs) has proved that poor spatial and temporal data coverage affects negatively and increases the uncertainty of the model estimation of seasonal and long-term trends. Finally, this study provides useful insights for more accurate trend calculation for future studies on marine primary production and ecosystem structure.
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