Ryan Bright
Research Professor
Biography
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Ryan Bright Stephanie Eisner Marianne Tronstad Lund Titta Majasalmi Gunnar Myhre Rasmus AstrupAbstract
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Ryan Bright Edouard Davin Thomas O'Halloran Julia Pongratz Kaiguang Zhao Alessandro CescattiAbstract
Following a land cover and land management change (LCMC), local surface temperature responds to both a change in available energy and a change in the way energy is redistributed by various non-radiative mechanisms. However, the extent to which non-radiative mechanisms contribute to the local direct temperature response for different types of LCMC across the world remains uncertain. Here, we combine extensive records of remote sensing and in situ observation to show that non-radiative mechanisms dominate the local response in most regions for eight of nine common LCMC perturbations. We find that forest cover gains lead to an annual cooling in all regions south of the upper conterminous United States, northern Europe, and Siberia—reinforcing the attractiveness of re-/afforestation as a local mitigation and adaptation measure in these regions. Our results affirm the importance of accounting for non-radiative mechanisms when evaluating local land-based mitigation or adaptation policies.
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«This is a perspective for the article 2016 Environ. Res. Lett. 11 095010”
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Tuva Grytli Geoffrey Guest Carine Lausselet Francesco Cherubini Ryan Bright Per Kr. Rørstad Helmer Belbo Rasmus Astrup Øyvind Skreiberg Morten Seljeskog Franziska Goile Anders Hammer StrømmanAbstract
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Ryan BrightAbstract
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Ryan Bright Clara Antón Fernández Rasmus Astrup Francesco Cherubini Maria Malene Kvalevåg Anders Hammer StrømmanAbstract
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Ryan Bright Francesco Cherubini Rasmus Astrup Clara Antón Fernández Anders Hammer Strømman Maria Malene KvalevågAbstract
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Ryan Bright Rasmus Astrup Anders Hammer Strømman Clara Antón-Fernández Maria Malene Kvalevåg Francesco CherubiniAbstract
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An 11-year remotely sensed surface albedo dataset coupled with historical meteorological and stand-level forest management data for a variety of stands in Norway’s most productive logging region is used to develop regression models describing temporal changes in forest albedo following clear-cut harvest disturbance events. Datasets are grouped by dominant tree species, and two alternate multiple regression models are developed and tested following a potential-modifier approach. This result in models with statistically significant parameters (p < 0.05) that explain a large proportion of the observed variation, requiring a single canopy modifier predictor coupled with either monthly or annual mean air temperature as a predictor of a stand’s potential albedo. Models based on annual mean temperature predict annual albedo with errors (RMSE) in the range of 0.025–0.027, while models based on monthly mean temperature predict monthly albedo with errors ranging between of 0.057–0.065 depending on the dominant tree species. While both models have the potential to be transferable to other boreal regions with similar forest management regimes, further validation efforts are required. As active management of boreal forests is increasingly seen as a means to mitigate climate change, the presented models can be used with routine forest inventory and meteorological data to predict albedo evolution in managed forests throughout the region, which, together with carbon cycle modeling, can lead to more holistic climate impact assessments of alternative forest harvest scenarios and forest product systems.
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Ryan Bright Francesco Cherubini Rasmus Astrup Neil Bird Annette L. Cowie Mark J. Ducey Gregg Marland Kim Pingoud Ilkka Savolainen Anders Hammer StrømmanAbstract
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