WWF RACER project

In december 2010, ARCTUS was contacted by the World Wildlife Foundation (WWF) and was requested services for their RACER project. RACER stands for “Rapid Assessment of features and areas for Circumarctic Ecosystem Resilience in the 21st Century” and is being executed by the various branches of WWF from all around the world. The aim of the project is to use scientific tools to identify the regions in the global arctic where the recent environmental changes have the most important influences.

PP_OConly_1998_Reg-1_ecoregAs part of their efforts to identify important areas for ecological function, areas of comparatively high ocean surface-layer primary productivity (PP) were attempted to be identified using monthly SeaWiFS chlorophyll-a image time series starting from 1998. Their efforts were partly successfull due to the limitations of the global chlorophyll-a algorithm and the default atmospheric correction that are well known to be present near coastal areas and polar regions.

The RACER project requested from ARCTUS expert guidance and data validation services for their data analysis methods. ARCTUS was able to provide WWF a reliable estimate of the biologic activity using a genuine PP model that is driven by various satellite images from different sources. The model integrates satellite radiance images from the SeaWiFS sensor with the chlorophyll-a concentration from ocean color images using a case-II algorithm and light availability information from meteorological satellites. The model estimates atmospheric effects using a radiative transfer model and involves an independently developed approach to estimate marine PP. For more information about the PP model, please contact Dr. Simon BĂ©langer.

monthlyPP1998-2010small150The yearly and monthly average PP images were computed at the pan-Arctic scale. For all the different ecoregions considered by the RACER study, total PP (Tg/year) was computed and reported. The monthly and yearly anomalies were determined and temporal trends were modeled by statistically decomposing the temporal variations into their components. Temporal trends were analyzed separately for each ecoregion in order to reveal regional differences in responses to climate changes.