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Subject: data interpretation × WGL Institute: PIK ×

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    Near-ubiquity of ice-edge blooms in the Arctic
    (Göttingen : Copernicus GmbH, 2011) Perrette, M.; Yool, A.; Quartly, G.D.; Popova, E.E.
    Ice-edge blooms are significant features of Arctic primary production, yet have received relatively little attention. Here we combine satellite ocean colour and sea-ice data in a pan-Arctic study. Ice-edge blooms occur in all seasonally ice-covered areas and from spring to late summer, being observed in 77-89% of locations for which adequate data exist, and usually peaking within 20 days of ice retreat. They sometimes form long belts along the ice-edge (greater than 100 km), although smaller structures were also found. The bloom peak is on average more than 1 mg m-3, with major blooms more than 10 mg m -3, and is usually located close to the ice-edge, though not always. Some propagate behind the receding ice-edge over hundreds of kilometres and over several months, while others remain stationary. The strong connection between ice retreat and productivity suggests that the ongoing changes in Arctic sea-ice may have a significant impact on higher trophic levels and local fish stocks.
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    A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)
    (München : European Geopyhsical Union, 2017) Forkel, Matthias; Dorigo, Wouter; Lasslop, Gitta; Teubner, Irene; Chuvieco, Emilio; Thonicke, Kirsten
    Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.