Inferring Surface Albedo Prediction Error Linked to Forest Structure at High Latitudes. Issue 10 (17th May 2018)

• Record Type: Journal Article
• Authors: Bright, Ryan M.; Eisner, Stephanie; Lund, Marianne T.; Majasalmi, Titta; Myhre, Gunnar; Astrup, Rasmus
• Is Part Of: Journal of geophysical research. Volume 123:Issue 10(2018)
• Page Start: 4910
• Page End: 4925
• Publication Date: 2018-05-17
• Subjects: forest management -- LULCC -- albedo feedback -- climate model -- land model -- boreal forest; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD028293
• Languages: English
• ISSNs: 2169-897X

Abstract

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 km 2 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/m 2 . 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. Plain Language Summary: Surface albedo—or the ratio of reflected to incoming sunlight—is an important physical property of the climate system and, as such, requires skillful prediction by climate models. Predicting the surface albedo in a forest during months with snow is complicated by many factors, among which is the sufficiency by which a forest's physical properties are represented in the model. This study makes use of detailed national forest inventory information to estimate the contribution to albedo prediction error that may be wholly attributed to insufficient characterizations of forest structure in a climate model, which we find to be nonnegligible. This study also investigates the consequence of ignoring structural transitions in forests when predicting future surface albedo impacts of forest management activities in climate modeling studies. For a case study region in southeast Norway, we find that not accounting for differences in surface albedo between forests in various development states can result in substantial climate effects going undetected, with magnitudes on the order of the annual greenhouse gas emissions of some European countries such as The Netherlands, or approximately 2 days of current global CO2 emissions. Key Points: Absolute surface albedo prediction error due to insufficient characterization of forest structure can be as large as 0.19 in Fennoscandia Winter (December–March) radiative forcing of albedo prediction error linked to forest structure is 0.4 W/m 2 for a spatial domain of 611, 000 km 2 Neglecting forest albedo transitions when simulating forest management can lead to large shortwave energy balance prediction error