Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
<p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spa...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-01-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/18/95/2021/bg-18-95-2021.pdf |
| _version_ | 1818412442756055040 |
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| author | P. Horvath P. Horvath H. Tang H. Tang H. Tang R. Halvorsen F. Stordal F. Stordal L. M. Tallaksen L. M. Tallaksen T. K. Berntsen T. K. Berntsen A. Bryn A. Bryn A. Bryn |
| author_facet | P. Horvath P. Horvath H. Tang H. Tang H. Tang R. Halvorsen F. Stordal F. Stordal L. M. Tallaksen L. M. Tallaksen T. K. Berntsen T. K. Berntsen A. Bryn A. Bryn A. Bryn |
| author_sort | P. Horvath |
| collection | DOAJ |
| description | <p>Vegetation is an important component in global ecosystems, affecting the
physical, hydrological and biogeochemical properties of the land
surface. Accordingly, the way vegetation is parameterized strongly influences
predictions of future climate by Earth system models. To capture future
spatial and temporal changes in vegetation cover and its feedbacks to the
climate system, dynamic global vegetation models (DGVMs) are included as
important components of land surface models. Variation in the predicted
vegetation cover from DGVMs therefore has large impacts on modelled radiative
and non-radiative properties, especially over high-latitude regions. DGVMs are
mostly evaluated by remotely sensed products and less often by other vegetation
products or by in situ field observations. In this study, we evaluate the
performance of three methods for spatial representation of present-day
vegetation cover with respect to prediction of plant functional type (PFT)
profiles – one based upon distribution models (DMs), one that uses a remote
sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT
profiles based on physiological and ecological processes, a DM relies on
statistical correlations between a set of predictors and the modelled target,
and the RS dataset is based on classification of spectral reflectance patterns
of satellite images. PFT profiles obtained from an independently collected
field-based vegetation dataset from Norway were used for the evaluation. We
found that RS-based PFT profiles matched the reference dataset best, closely
followed by DM, whereas predictions from DGVMs often deviated strongly from the
reference. DGVM predictions overestimated the area covered by boreal
needleleaf evergreen trees and bare ground at the expense of boreal broadleaf
deciduous trees and shrubs. Based on environmental predictors identified by DM
as important, three new environmental variables (e.g. minimum temperature in
May, snow water equivalent in October and precipitation seasonality) were
selected as the threshold for the establishment of these high-latitude
PFTs. We performed a series of sensitivity experiments to investigate if these
thresholds improve the performance of the DGVM method. Based on our results, we
suggest implementation of one of these novel PFT-specific thresholds (i.e.
precipitation seasonality) in the DGVM method. The results highlight the potential of
using PFT-specific thresholds obtained by DM in development of DGVMs in
broader regions. Also, we emphasize the potential of establishing DMs as a
reliable method for providing PFT distributions for evaluation of DGVMs
alongside RS.</p> |
| first_indexed | 2024-12-14T10:47:23Z |
| format | Article |
| id | doaj.art-afffa6f476b4475d9f5cbea74927dce3 |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-12-14T10:47:23Z |
| publishDate | 2021-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-afffa6f476b4475d9f5cbea74927dce32022-12-21T23:05:24ZengCopernicus PublicationsBiogeosciences1726-41701726-41892021-01-01189511210.5194/bg-18-95-2021Improving the representation of high-latitude vegetation distribution in dynamic global vegetation modelsP. Horvath0P. Horvath1H. Tang2H. Tang3H. Tang4R. Halvorsen5F. Stordal6F. Stordal7L. M. Tallaksen8L. M. Tallaksen9T. K. Berntsen10T. K. Berntsen11A. Bryn12A. Bryn13A. Bryn14Geo-Ecology Research Group, Natural History Museum, University of Oslo, P.O. Box 1172, Blindern, 0318 Oslo, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwayGeo-Ecology Research Group, Natural History Museum, University of Oslo, P.O. Box 1172, Blindern, 0318 Oslo, NorwaySection of Meteorology and Oceanography, Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwayGeo-Ecology Research Group, Natural History Museum, University of Oslo, P.O. Box 1172, Blindern, 0318 Oslo, NorwaySection of Meteorology and Oceanography, Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwaySection for Geography and Hydrology, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwaySection of Meteorology and Oceanography, Department of Geosciences, University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, NorwayGeo-Ecology Research Group, Natural History Museum, University of Oslo, P.O. Box 1172, Blindern, 0318 Oslo, NorwayDivision of Survey and Statistics, Norwegian Institute of Bioeconomy Research, P.O. Box 115, 1431 Ås, NorwayLATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway<p>Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.</p>https://bg.copernicus.org/articles/18/95/2021/bg-18-95-2021.pdf |
| spellingShingle | P. Horvath P. Horvath H. Tang H. Tang H. Tang R. Halvorsen F. Stordal F. Stordal L. M. Tallaksen L. M. Tallaksen T. K. Berntsen T. K. Berntsen A. Bryn A. Bryn A. Bryn Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models Biogeosciences |
| title | Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models |
| title_full | Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models |
| title_fullStr | Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models |
| title_full_unstemmed | Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models |
| title_short | Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models |
| title_sort | improving the representation of high latitude vegetation distribution in dynamic global vegetation models |
| url | https://bg.copernicus.org/articles/18/95/2021/bg-18-95-2021.pdf |
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