Assessing uncertainties in land cover projections

Peter Alexander, Reinhard Prestele, Peter H. Verburg, Almut Arneth, Claudia Baranzelli, Filipe Batista e Silva, Calum Brown, Adam Butler, Katherine Calvin, Nicolas Dendoncker, Jonathan C. Doelman, Robert Dunford, Kerstin Engström, David Eitelberg, Shinichiro Fujimori, Paula A. Harrison, Tomoko Hasegawa, Petr Havlik, Sascha Holzhauer, Florian HumpenöderChris Jacobs-Crisioni, Atul K. Jain, Tamás Krisztin, Page Kyle, Carlo Lavalle, Tim Lenton, Jiayi Liu, Prasanth Meiyappan, Alexander Popp, Tom Powell, Ronald D. Sands, Rüdiger Schaldach, Elke Stehfest, Jevgenijs Steinbuks, Andrzej Tabeau, Hans van Meijl, Marshall A. Wise, Mark D A Rounsevell

Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs

Résumé

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.

langue originaleAnglais
Pages (de - à)767-781
Nombre de pages15
journalGlobal Change Biology
Volume23
Numéro de publication2
Les DOIs
Etat de la publicationPublié - 1 févr. 2017

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