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| Titel |
Radiative forcing bias of simulated surface albedo modifications linked to forest cover changes at northern latitudes |
| VerfasserIn |
R. M. Bright, G. Myhre, R. Astrup, C. Antón-Fernández, A. H. Strømman |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 12, no. 7 ; Nr. 12, no. 7 (2015-04-15), S.2195-2205 |
| Datensatznummer |
250117896
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| Publikation (Nr.) |
 copernicus.org/bg-12-2195-2015.pdf |
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| Zusammenfassung |
In the presence of snow, the bias in the prediction of surface albedo by
many climate models remains difficult to correct due to the difficulties of
separating the albedo parameterizations from those describing snow and
vegetation cover and structure. This can be overcome by extracting the
albedo parameterizations in isolation, by executing them with observed
meteorology and information on vegetation structure, and by comparing the
resulting predictions to observations. Here, we employ an empirical data set
of forest structure and daily meteorology for three snow cover seasons and
for three case regions in boreal Norway to compute and evaluate predicted
albedo to those based on daily MODIS retrievals. Forest and adjacent open
area albedos are subsequently used to estimate bias in top-of-the-atmosphere
(TOA) radiative forcings (RF) from albedo changes (Δα, Open–Forest)
connected to land use and land cover changes (LULCC).
As expected, given the diversity of approaches by which snow masking by
tall-statured vegetation is parameterized, the magnitude and sign of the
albedo biases varied considerably for forests. Large biases at the open
sites were also detected, which was unexpected given that these sites were
snow-covered throughout most of the analytical time period, therefore
eliminating potential biases linked to snow-masking parameterizations.
Biases at the open sites were mostly positive, exacerbating the strength of
vegetation masking effects and hence the simulated LULCC Δα
RF. Despite the large biases in both forest and open area albedos by some
schemes in some months and years, the mean Δα RF bias over
the 3-year period (November–May) was considerably small across models
(−2.1 ± 1.04 Wm−2; 21 ± 11%); four of six models had
normalized mean absolute errors less than 20%. Identifying systematic
sources of the albedo prediction biases proved challenging, although for
some schemes clear sources were identified. |
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