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Assessing uncertainties in land cover projections

Alexander, Peter; Prestele, Reinhard; Verburg, Peter H.; Arneth, Almut LU ; Baranzelli, Claudia; Batista e Silva, Filipe; Brown, Calum; Butler, Adam; Calvin, Katherine and Dendoncker, Nicolas, et al. (2016) In Global Change Biology
Abstract

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the... (More)

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.

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organization
publishing date
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Contribution to journal
publication status
epub
subject
keywords
Cropland, Land cover, Land use, Model inter-comparison, Uncertainty
in
Global Change Biology
publisher
Wiley-Blackwell
external identifiers
  • Scopus:84982237065
ISSN
1354-1013
DOI
10.1111/gcb.13447
language
English
LU publication?
yes
id
09e079c3-d0b6-444a-bc29-082728d0eeb6
date added to LUP
2016-10-10 09:52:08
date last changed
2016-10-27 08:55:58
@misc{09e079c3-d0b6-444a-bc29-082728d0eeb6,
  abstract     = {<p>Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.</p>},
  author       = {Alexander, Peter and Prestele, Reinhard and Verburg, Peter H. and Arneth, Almut and Baranzelli, Claudia and Batista e Silva, Filipe and Brown, Calum and Butler, Adam and Calvin, Katherine and Dendoncker, Nicolas and Doelman, Jonathan C. and Dunford, Robert and Engstrom, Kerstin and Eitelberg, David and Fujimori, Shinichiro and Harrison, Paula A. and Hasegawa, Tomoko and Havlik, Petr and Holzhauer, Sascha and Humpenöder, Florian and Jacobs-Crisioni, Chris and Jain, Atul K. and Krisztin, Tamás and Kyle, Page and Lavalle, Carlo and Lenton, Tim and Liu, Jiayi and Meiyappan, Prasanth and Popp, Alexander and Powell, Tom and Sands, Ronald D. and Schaldach, Rüdiger and Stehfest, Elke and Steinbuks, Jevgenijs and Tabeau, Andrzej and van Meijl, Hans and Wise, Marshall A. and Rounsevell, Mark D A},
  issn         = {1354-1013},
  keyword      = {Cropland,Land cover,Land use,Model inter-comparison,Uncertainty},
  language     = {eng},
  month        = {08},
  publisher    = {ARRAY(0x9916f70)},
  series       = {Global Change Biology},
  title        = {Assessing uncertainties in land cover projections},
  url          = {http://dx.doi.org/10.1111/gcb.13447},
  year         = {2016},
}