Ryan Bright

Research Professor

(+47) 974 77 997
ryan.bright@nibio.no

Place
Ås H8

Visiting address
Høgskoleveien 8, 1433 Ås

Biography

Research interests: - Ecological climatology - Albedo dynamics in boreal forests - Land surface modeling - Climate metrics for agriculture, forestry and other land use activities

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Abstract

Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MSNFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

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Abstract

Predicting the surface albedo of a forest of a given species composition or plant functional type is complicated by the wide range of structural attributes it may display. Accurate characterizations of forest structure are therefore essential to reducing the uncertainty of albedo predictions in forests, particularly in the presence of snow. At present, forest albedo parameterizations remain a nonnegligible source of uncertainty in climate models, and the magnitude attributable to insufficient characterization of forest structure remains unclear. Here we employ a forest classification scheme based on the assimilation of Fennoscandic (i.e., Norway, Sweden, and Finland) national forest inventory data to quantify the magnitude of the albedo prediction error attributable to poor characterizations of forest structure. For a spatial domain spanning ~611,000 km2 of boreal forest, we find a mean absolute wintertime (December–March) albedo prediction error of 0.02, corresponding to a mean absolute radiative forcing ~0.4 W/m2. Further, we evaluate the implication of excluding albedo trajectories linked to structural transitions in forests during transient simulations of anthropogenic land use/land cover change. We find that, for an intensively managed forestry region in southeastern Norway, neglecting structural transitions over the next quarter century results in a foregone (undetected) radiatively equivalent impact of ~178 Mt‐CO2‐eq. year−1 on average during this period—a magnitude that is roughly comparable to the annual greenhouse gas emissions of a country such as The Netherlands. Our results affirm the importance of improving the characterization of forest structure when simulating surface albedo and associated climate effects.

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Abstract

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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Abstract

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.