Articles | Volume 18, issue 1
https://doi.org/10.5194/bg-18-95-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-95-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2021
Research article |
| 07 Jan 2021
| 07 Jan 2021
Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Rune Halvorsen
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Frode Stordal
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Lena Merete Tallaksen
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Section for Geography and Hydrology, Department of
Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Terje Koren Berntsen
Section of Meteorology and Oceanography, Department of Geosciences,
University of Oslo, P.O. Box 1022, Blindern, 0315 Oslo, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Anders Bryn
Geo-Ecology Research Group, Natural History Museum, University of
Oslo, P.O. Box 1172, Blindern, 0318 Oslo, Norway
Division of Survey and Statistics, Norwegian Institute of
Bioeconomy Research, P.O. Box 115, 1431 Ås, Norway
LATICE Research Group, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo,
Norway
Related authors
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
Short summary
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1391, https://doi.org/10.5194/egusphere-2025-1391, 2025
Short summary
Short summary
When flood happen during, or shortly after, droughts, the impacts of can be magnified. In hydrological research, defining these events can be challenging. Here we have tried to address some of the challenges defining these events using real-world examples. We show how different methodological approaches differ in their results, make suggestions on when to use which approach, and outline some pitfalls of which researchers should be aware.
Astrid Vatne, Norbert Pirk, Kolbjørn Engeland, Ane Victoria Vollsnes, and Lena Merete Tallaksen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1140, https://doi.org/10.5194/egusphere-2025-1140, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Measurements of evaporation are important to understand how evaporation modifies the water balance of northern ecosystems. However, evaporation data in these regions are scarce. We explored a new dataset of evaporation measurements from four wetland sites in Norway and found that up to 30 % of the annual precipitation evaporate back to the atmosphere. Our results indicate that earlier snow melt-out and drier air can increase annual evaporation in the region.
Ragnhild Bieltvedt Skeie, Magne Aldrin, Terje K. Berntsen, Marit Holden, Ragnar Bang Huseby, Gunnar Myhre, and Trude Storelvmo
Earth Syst. Dynam., 15, 1435–1458, https://doi.org/10.5194/esd-15-1435-2024, https://doi.org/10.5194/esd-15-1435-2024, 2024
Short summary
Short summary
Climate sensitivity and aerosol forcing are central quantities in climate science that are uncertain and contribute to the spread in climate projections. To constrain them, we use observations of temperature and ocean heat content as well as prior knowledge of radiative forcings over the industrialized period. The estimates are sensitive to how aerosol cooling evolved over the latter part of the 20th century, and a strong aerosol forcing trend in the 1960s–1970s is not supported by our analysis.
Elin Ristorp Aas, Inge Althuizen, Hui Tang, Sonya Geange, Eva Lieungh, Vigdis Vandvik, and Terje Koren Berntsen
Biogeosciences, 21, 3789–3817, https://doi.org/10.5194/bg-21-3789-2024, https://doi.org/10.5194/bg-21-3789-2024, 2024
Short summary
Short summary
We used a soil model to replicate two litterbag decomposition experiments to examine the implications of climate, litter quality, and soil microclimate representation. We found that macroclimate was more important than litter quality for modeled mass loss. By comparing different representations of soil temperature and moisture we found that using observed data did not improve model results. We discuss causes for this and suggest possible improvements to both the model and experimental design.
Riccardo Biella, Ansastasiya Shyrokaya, Monica Ionita, Raffaele Vignola, Samuel Sutanto, Andrijana Todorovic, Claudia Teutschbein, Daniela Cid, Maria Carmen Llasat, Pedro Alencar, Alessia Matanó, Elena Ridolfi, Benedetta Moccia, Ilias Pechlivanidis, Anne van Loon, Doris Wendt, Elin Stenfors, Fabio Russo, Jean-Philippe Vidal, Lucy Barker, Mariana Madruga de Brito, Marleen Lam, Monika Bláhová, Patricia Trambauer, Raed Hamed, Scott J. McGrane, Serena Ceola, Sigrid Jørgensen Bakke, Svitlana Krakovska, Viorica Nagavciuc, Faranak Tootoonchi, Giuliano Di Baldassarre, Sandra Hauswirth, Shreedhar Maskey, Svitlana Zubkovych, Marthe Wens, and Lena Merete Tallaksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2069, https://doi.org/10.5194/egusphere-2024-2069, 2024
Short summary
Short summary
This research by the Drought in the Anthropocene (DitA) network highlights gaps in European drought management exposed by the 2022 drought and proposes a new direction. Using a Europe-wide survey of water managers, we examine four areas: increasing drought risk, impacts, drought management strategies, and their evolution. Despite growing risks, management remains fragmented and short-term. However, signs of improvement suggest readiness for change. We advocate for a European Drought Directive.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Norbert Pirk, Kristoffer Aalstad, Yeliz A. Yilmaz, Astrid Vatne, Andrea L. Popp, Peter Horvath, Anders Bryn, Ane Victoria Vollsnes, Sebastian Westermann, Terje Koren Berntsen, Frode Stordal, and Lena Merete Tallaksen
Biogeosciences, 20, 2031–2047, https://doi.org/10.5194/bg-20-2031-2023, https://doi.org/10.5194/bg-20-2031-2023, 2023
Short summary
Short summary
We measured the land–atmosphere exchange of CO2 and water vapor in alpine Norway over 3 years. The extremely snow-rich conditions in 2020 reduced the total annual evapotranspiration to 50 % and reduced the growing-season carbon assimilation to turn the ecosystem from a moderate annual carbon sink to an even stronger source. Our analysis suggests that snow cover anomalies are driving the most consequential short-term responses in this ecosystem’s functioning.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Norbert Pirk, Kristoffer Aalstad, Sebastian Westermann, Astrid Vatne, Alouette van Hove, Lena Merete Tallaksen, Massimo Cassiani, and Gabriel Katul
Atmos. Meas. Tech., 15, 7293–7314, https://doi.org/10.5194/amt-15-7293-2022, https://doi.org/10.5194/amt-15-7293-2022, 2022
Short summary
Short summary
In this study, we show how sparse and noisy drone measurements can be combined with an ensemble of turbulence-resolving wind simulations to estimate uncertainty-aware surface energy exchange. We demonstrate the feasibility of this drone data assimilation framework in a series of synthetic and real-world experiments. This new framework can, in future, be applied to estimate energy and gas exchange in heterogeneous landscapes more representatively than conventional methods.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
Short summary
Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Sara Marie Blichner, Moa Kristina Sporre, and Terje Koren Berntsen
Atmos. Chem. Phys., 21, 17243–17265, https://doi.org/10.5194/acp-21-17243-2021, https://doi.org/10.5194/acp-21-17243-2021, 2021
Short summary
Short summary
In this study we quantify how a new way of modeling the formation of new particles in the atmosphere affects the estimated cooling from aerosol–cloud interactions since pre-industrial times. Our improved scheme merges two common approaches to aerosol modeling: a sectional scheme for treating early growth and the pre-existing modal scheme in NorESM. We find that the cooling from aerosol–cloud interactions since pre-industrial times is reduced by 10 % when the new scheme is used.
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Lisa Emberson, Connie O'Neill, Frode Stordal, and Terje Koren Berntsen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-260, https://doi.org/10.5194/bg-2021-260, 2021
Revised manuscript not accepted
Short summary
Short summary
Subarctic vegetation is threatened by climate change and ozone. We assess essential climate variables in 2018/19. 2018 was warmer and brighter than usual in Spring with forest fires and elevated ozone in summer. Visible damage was observed on plant species in 2018. We find that generic parameterizations used in modeling ozone dose do not suffice. We propose a method to acclimate these parameterizations and find an ozone-induced biomass loss of 2.5 to 17.4 % (up to 6 % larger than default).
Stefanie Falk, Ane V. Vollsnes, Aud B. Eriksen, Frode Stordal, and Terje Koren Berntsen
Atmos. Chem. Phys., 21, 15647–15661, https://doi.org/10.5194/acp-21-15647-2021, https://doi.org/10.5194/acp-21-15647-2021, 2021
Short summary
Short summary
We evaluate regional and global models for ozone modeling and damage risk mapping of vegetation over subarctic Europe. Our analysis suggests that low-resolution global models do not reproduce the observed ozone seasonal cycle at ground level, underestimating ozone by 30–50 %. High-resolution regional models capture the seasonal cycle well, still underestimating ozone by up to 20 %. Our proposed gap-filling method for site observations shows a 76 % accuracy compared to the regional model (80 %).
Sara M. Blichner, Moa K. Sporre, Risto Makkonen, and Terje K. Berntsen
Geosci. Model Dev., 14, 3335–3359, https://doi.org/10.5194/gmd-14-3335-2021, https://doi.org/10.5194/gmd-14-3335-2021, 2021
Short summary
Short summary
Aerosol–cloud interactions are the largest contributor to climate forcing uncertainty. In this study we combine two common approaches to aerosol representation in global models: a sectional scheme, which is closer to first principals, for the smallest particles forming in the atmosphere and a log-modal scheme, which is faster, for the larger particles. With this approach, we improve the aerosol representation compared to observations, while only increasing the computational cost by 15 %.
Øyvind Seland, Mats Bentsen, Dirk Olivié, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yan-Chun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafsahl Karset, Oskar Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz
Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, https://doi.org/10.5194/gmd-13-6165-2020, 2020
Short summary
Short summary
The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. The temperature and precipitation patterns has improved compared to NorESM1. The model reaches present-day warming levels to within 0.2 °C of observed temperature but with a delayed warming during the late 20th century. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period of 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.1, and 3.9 K.
Sigrid J. Bakke, Monica Ionita, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 24, 5621–5653, https://doi.org/10.5194/hess-24-5621-2020, https://doi.org/10.5194/hess-24-5621-2020, 2020
Short summary
Short summary
This study provides an in-depth analysis of the 2018 northern European drought. Large parts of the region experienced 60-year record-breaking temperatures, linked to high-pressure systems and warm surrounding seas. Meteorological drought developed from May and, depending on local conditions, led to extreme low flows and groundwater drought in the following months. The 2018 event was unique in that it affected most of Fennoscandia as compared to previous droughts.
Marianne T. Lund, Borgar Aamaas, Camilla W. Stjern, Zbigniew Klimont, Terje K. Berntsen, and Bjørn H. Samset
Earth Syst. Dynam., 11, 977–993, https://doi.org/10.5194/esd-11-977-2020, https://doi.org/10.5194/esd-11-977-2020, 2020
Short summary
Short summary
Achieving the Paris Agreement temperature goals requires both near-zero levels of long-lived greenhouse gases and deep cuts in emissions of short-lived climate forcers (SLCFs). Here we quantify the near- and long-term global temperature impacts of emissions of individual SLCFs and CO2 from 7 economic sectors in 13 regions in order to provide the detailed knowledge needed to design efficient mitigation strategies at the sectoral and regional levels.
Cited articles
Ahti, T., Hämet-Ahti, L., and Jalas, J.: Vegetation zones and their sections in northwestern Europe, Ann. Bot. Fenn., 5, 169–211, 1968.
Alexander, R. and Millington, A. C.: Vegetation mapping: From Patch to
Planet, in: Vegetation Mapping, John Wiley and Sons, LTD, Chichester,
England, 321–331, 2000.
Álvarez-Martínez, J. M., Jiménez-Alfaro, B., Barquín, J.,
Ondiviela, B., Recio, M., Silió-Calzada, A., and Juanes, J. A.:
Modelling the area of occupancy of habitat types with remote sensing,
Methods Ecol. Evol., 9, 580–593, https://doi.org/10.1111/2041-210X.12925, 2018.
Assal, T. J., Anderson, P. J., and Sibold, J.: Mapping forest functional
type in a forest-shrubland ecotone using SPOT imagery and predictive habitat
distribution modelling, Remote Sens. Lett., 6, 755–764, https://doi.org/10.1080/2150704x.2015.1072289, 2015.
Bakkestuen, V., Erikstad, L., and Halvorsen, R.: Step-less models for
regional environmental variation in Norway, J. Biogeogr., 35,
1906–1922, https://doi.org/10.1111/j.1365-2699.2008.01941.x,
2008.
Bjordal, J.: Potential Implications of Lichen Cover for the Surface Energy
Balance: Implementing Lichen as a new Plant Functional Type in the Community
Land Model (CLM4.5), Master Thesis, Department of Geosciences, University of
Oslo, Oslo, 99 pp., 2018.
Bohn, U., Gollub, G., Hettwer, C., Neuhäuslova, Z., Raus, T.,
Schlüter, H., and Weber, H.: Map of the Natural Vegetation of Europe,
Scale 1 : 2 500 000, Federal Agency for Nature Conservation, Münster,
2000.
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as
patches of plant functional types: An integrating concept for climate and
ecosystem models, Global Biogeochem. Cy., 16, 5-1–5-23, https://doi.org/10.1029/2000gb001360, 2002.
Bonan, G. B., Levis, S., Sitch, S., Vertenstein, M., and Oleson, K. W.: A
dynamic global vegetation model for use with climate models: concepts and
description of simulated vegetation dynamics, Glob. Change Biol., 9,
1543–1566, https://doi.org/10.1046/j.1365-2486.2003.00681.x,
2003.
Bonan, G. B.: Forests, Climate, and Public Policy: A 500-Year
Interdisciplinary Odyssey, Annu. Rev. Ecol. Evol. S., 47, 97–121, https://doi.org/10.1146/annurev-ecolsys-121415-032359, 2016.
Bryn, A., Dramstad, W., Fjellstad, W., and Hofmeister, F.: Rule-based
GIS-modelling for management purposes: A case study from the islands of
Froan, Sør-Trøndelag, mid-western Norway, Norsk Geogr. Tidsskr., 64, 175–184, https://doi.org/10.1080/00291951.2010.528224, 2010.
Bryn, A., Dourojeanni, P., Hemsing, L. Ø., and O'Donnell, S.: A
high-resolution GIS null model of potential forest expansion following land
use changes in Norway, Scand. J. Forest Res., 28, 81–98,
https://doi.org/10.1080/02827581.2012.689005, 2013.
Bryn, A., Strand, G.-H., Angeloff, M., and Rekdal, Y.: Land cover in Norway
based on an area frame survey of vegetation types, Norsk Geogr.
Tidsskr., 72, 1–15, https://doi.org/10.1080/00291951.2018.1468356, 2018.
Chadburn, S. E., Burke, E. J., Essery, R. L. H., Boike, J., Langer, M., Heikenfeld, M., Cox, P. M., and Friedlingstein, P.: Impact of model developments on present and future simulations of permafrost in a global land-surface model, The Cryosphere, 9, 1505–1521, https://doi.org/10.5194/tc-9-1505-2015, 2015.
Coppell, R., Gloor, E., and Holden, J.: A process-based Sphagnum
plant-functional-type model for implementation in the TRIFFID Dynamic Global
Vegetation Model, Geosci. Model Dev. Discuss.,
https://doi.org/10.5194/gmd-2019-51, in review, 2019.
Czekanowski, J.: Zur differentialdiagnose der Neandertalgruppe, Friedr.
Vieweg and Sohn, 1909.
Dallmeyer, A., Claussen, M., and Brovkin, V.: Harmonising plant functional type distributions for evaluating Earth system models, Clim. Past, 15, 335–366, https://doi.org/10.5194/cp-15-335-2019, 2019.
Davin, E. L. and de Noblet-Ducoudré, N.: Climatic Impact of
Global-Scale Deforestation: Radiative versus Nonradiative Processes, J.
Climate, 23, 97–112, https://doi.org/10.1175/2009jcli3102.1,
2010.
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D.,
Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M.,
Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L.,
Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Kloser, H., Shennan-Farpon, Y., Kindt, R., Lilleso, J. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F., and Saleem, M.: An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm,
Bioscience, 67, 534–545, https://doi.org/10.1093/biosci/bix014,
2017.
Druel, A., Peylin, P., Krinner, G., Ciais, P., Viovy, N., Peregon, A., Bastrikov, V., Kosykh, N., and Mironycheva-Tokareva, N.: Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0), Geosci. Model Dev., 10, 4693–4722, https://doi.org/10.5194/gmd-10-4693-2017, 2017.
Druel, A., Ciais, P., Krinner, G., and Peylin, P.: Modeling the Vegetation
Dynamics of Northern Shrubs and Mosses in the ORCHIDEE Land Surface Model,
J. Adv. Model. Earth Sy., 11, 2020–2035, https://doi.org/10.1029/2018ms001531, 2019.
Duveiller, G., Hooker, J., and Cescatti, A.: The mark of vegetation change
on Earth's surface energy balance, Nat. Commun., 9, 679, https://doi.org/10.1038/s41467-017-02810-8, 2018.
Dyrrdal, A. V., Stordal, F., and Lussana, C.: Evaluation of summer
precipitation from EURO-CORDEX fine-scale RCM simulations over Norway,
Int. J. Climatol., 38, 1661–1677, https://doi.org/10.1002/joc.5287, 2018.
Eurostat: The Lucas Survey: European Statisticians Monitor Territory, Office
for Official Publications of the European Communities, Luxembourg, 2003.
Euskirchen, E. S., McGuire, A. D., Chapin III, F. S., Yi, S., and Thompson, C. C.: Changes in vegetation in northern Alaska under scenarios of climate
change, 2003–2100: implications for climate feedbacks, Ecol.
Appl., 19, 1022–1043, https://doi.org/10.1890/08-0806.1,
2009.
Ferrier, S. and Guisan, A.: Spatial modelling of biodiversity at the
community level, J. Appl. Ecol., 43, 393–404, https://doi.org/10.1111/j.1365-2664.2006.01149.x, 2006.
Ferrier, S., Watson, G., Pearce, J., and Drielsma, M.: Extended statistical
approaches to modelling spatial pattern in biodiversity in northeast New
South Wales. I. Species-level modelling, Conserv. Biol., 11,
2275–2307, https://doi.org/10.1023/a:1021302930424, 2002.
Fielding, A. H. and Bell, J. F.: A review of methods for the assessment of
prediction errors in conservation presence/absence models, Environ.
Conserv., 24, 38–49, 1997.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Ian Woodward, F.: Assessing uncertainties in
a second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681, https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Franklin, S. E. and Wulder, M. A.: Remote sensing methods in medium spatial
resolution satellite data land cover classification of large areas, Prog.
Phys. Geogr., 26, 173–205, https://doi.org/10.1191/0309133302pp332ra, 2002.
Førland, E.: Precipitation and topography, Klima, 79, 23–24, 1979 (in Norwegian with English
summary).
Gotangco Castillo, C. K., Levis, S., and Thornton, P.: Evaluation of the New
CNDV Option of the Community Land Model: Effects of Dynamic Vegetation and
Interactive Nitrogen on CLM4 Means and Variability, J. Climate, 25,
3702–3714, https://doi.org/10.1175/jcli-d-11-00372.1, 2012.
Halvorsen, R.: A gradient analytic perspective on distribution modelling,
Sommerfeltia, 35, 1–165, https://doi.org/10.2478/v10208-011-0015-3, 2012.
Hanssen-Bauer, I., Førland, E., Haddeland, I., Hisdal, H., Lawrence, D.,
Mayer, S., Nesje, A., Nilsen, J., Sandven, S., and Sandø, A.: Climate in
Norway 2100–A knowledge base for climate adaptation, The Norwegian Centre
for Climate Services, Oslo, 2017.
Hartley, A. J., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty
in plant functional type distributions and its impact on land surface
models, Remote Sens. Environ., 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037, 2017.
Hemsing, L. Ø. and Bryn, A.: Three methods for modelling potential
natural vegetation (PNV) compared: A methodological case study from
south-central Norway, Norsk Geogr. Tidsskr., 66, 11–29, https://doi.org/10.1080/00291951.2011.644321, 2012.
Henderson, E. B., Ohmann, J. L., Gregory, M. J., Roberts, H. M., and Zald, H.: Species distribution modelling for plant communities: stacked single
species or multivariate modelling approaches?, Appl. Veg. Sci.,
17, 516–527, https://doi.org/10.1111/avsc.12085, 2014.
Hengl, T., Walsh, M. G., Sanderman, J., Wheeler, I., Harrison, S. P., and
Prentice, I. C.: Global mapping of potential natural vegetation: an
assessment of machine learning algorithms for estimating land potential,
PeerJ, 6, e5457, https://doi.org/10.7717/peerj.5457, 2018.
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L.,
Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kuhn, I., and Sykes, M. T.: Projecting the future distribution of European potential natural
vegetation zones with a generalized, tree species-based dynamic vegetation
model, Global Ecol. Biogeogr., 21, 50–63, https://doi.org/10.1111/j.1466-8238.2010.00613.x, 2012.
Hijmans, R. J.: Geographic Data Analysis and Modeling, retrieved from: https://CRAN.R-project.org/package=raster, last access: 30 January 2019.
Horvath, P.: geco-nhm/DGVM_RS_DM_Norway: First release, Zenodo, https://doi.org/10.5281/zenodo.4399235, 2020.
Horvath, P., Halvorsen, R., Stordal, F., Tallaksen, L. M., Tang, H., and
Bryn, A.: Distribution modelling of vegetation types based on area frame
survey data, Appl. Veg. Sci., 22, 547–560, https://doi.org/10.1111/avsc.12451, 2019.
Horvath, P., Tang, H., Halvorsen, R., Stordal, F., Merete Tallaksen, L., Berntsen, T. K., and Bryn, A.: High-resolution DM-based and RS-based PFT maps, Dryad, https://doi.org/10.5061/dryad.dfn2z34xn, 2020.
Johansen, B. E.: Satellittbasert vegetasjonskartlegging for Norge,
Direktoratet for Naturforvaltning, Norsk Romsenter, 2009.
Keith, D. A., Ferrer, J. R., Nicholson, E., Bishop, M. J., Polidoro, B. A.,
Llodra, E. R., Tozer, M. G., Nel, J. L., Nally, R. M., Gregr, E. J.,
Watermeyer, K. E., Essl, F., Faber-Langendoen, D., Franklin, J., Lehmann, C. E. R., Etter, A., Roux, D. J., Stark, J. S., Rowland, J. A., Brummitt, N. A., Fernandez-Arcaya, U. C., Suthers, I. M., Wiser, S. K., Donohue, I.,
Jackson, L. J., Pennington, R. T., Pettorelli, N., Andrade, A., Kontula, T.,
Lindgaard, A., Tahvanainan, T., Terauds, A., Venter, O., Watson, J. E. M.,
Chadwick, M. A., Murray, N. J., Moat, J., Pliscoff, P., Zager, I., and
Kingsford, R. T.: The IUCN Global Ecosystem Typology v1.01: Descriptive
profiles for Biomes and Ecosystem Functional Groups, IUCN, CEM, New York,
172, 2020.
Lantz, T. C., Gergel, S. E., and Kokelj, S. V.: Spatial Heterogeneity in the
Shrub Tundra Ecotone in the Mackenzie Delta Region, Northwest Territories:
Implications for Arctic Environmental Change, Ecosystems, 13, 194–204,
https://doi.org/10.1007/s10021-009-9310-0, 2010.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Yang, Z. L., Levis, S., and Sakaguchi, K.:
Parameterization improvements and functional and structural advances in
version 4 of the Community Land Model, J. Adv. Model. Earth
Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land
surface in the Community Land Model (CLM 3.0), J. Geophys.
Res., 112, G01023, https://doi.org/10.1029/2006jg000168,
2007.
Levis, S., Bonan, B., Vertenstein, M., and Oleson, K.: The community land
model's dynamic global vegetation model (CLM-DGVM): technical description
and user's guide, National Center for Atmospheric Research, Boulder,
Colorado, 2004.
Li, W., Ciais, P., MacBean, N., Peng, S., Defourny, P., and Bontemps, S.:
Major forest changes and land cover transitions based on plant functional
types derived from the ESA CCI Land Cover product, Int. J.
Appl. Earth Obs., 47, 30–39, https://doi.org/10.1016/j.jag.2015.12.006, 2016.
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018.
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018a.
Lussana, C., Tveito, O., and Uboldi, F.: Three-dimensional spatial
interpolation of 2 m temperature over Norway, Q. J. Roy.
Meteor. Soc., 144, 344–364, https://doi.org/10.1002/qj.3208, 2018b.
Majasalmi, T., Eisner, S., Astrup, R., Fridman, J., and Bright, R. M.: An enhanced forest classification scheme for modeling vegetation–climate interactions based on national forest inventory data, Biogeosciences, 15, 399–412, https://doi.org/10.5194/bg-15-399-2018, 2018.
Miller, P. A. and Smith, B.: Modelling Tundra Vegetation Response to Recent
Arctic Warming, AMBIO, 41, 281–291, https://doi.org/10.1007/s13280-012-0306-1, 2012.
Moen, A.: Vegetation, Norwegian Mapping Authority, Hønefoss, 200 pp.,
1999.
Mücher, C. A., Hennekens, S. M., Bunce, R. G. H., Schaminée, J. H. J., and Schaepman, M. E.: Modelling the spatial distribution of Natura 2000
habitats across Europe, Landscape Urban Plan., 92, 148–159,
https://doi.org/10.1016/j.landurbplan.2009.04.003, 2009.
Myers-Smith, I. H., Forbes, B. C., Wilmking, M., Hallinger, M., Lantz, T.,
Blok, D., Tape, K. D., Macias-Fauria, M., Sass-Klaassen, U., Lévesque, E., Boudreau, S., Ropars, P., Hermanutz, L., Trant, A., Collier, L. S.,
Weijers, S., Rozema, J., Rayback, S. A., Schmidt, N. M., Schaepman-Strub, G., Wipf, S., Rixen, C., Ménard, C. B., Venn, S., Goetz, S.,
Andreu-Hayles, L., Elmendorf, S., Ravolainen, V., Welker, J., Grogan, P.,
Epstein, H. E., and Hik, D. S.: Shrub expansion in tundra ecosystems:
dynamics, impacts and research priorities, Environ. Res. Lett., 6, 045509,
https://doi.org/10.1088/1748-9326/6/4/045509, 2011.
Myers-Smith, I. H., Kerby, J. T., Phoenix, G. K., Bjerke, J. W., Epstein, H. E., Assmann, J. J., John, C., Andreu-Hayles, L., Angers-Blondin, S., Beck, P. S. A., Berner, L. T., Bhatt, U. S., Bjorkman, A. D., Blok, D., Bryn, A.,
Christiansen, C. T., Cornelissen, J. H. C., Cunliffe, A. M., Elmendorf, S. C., Forbes, B. C., Goetz, S. J., Hollister, R. D., de Jong, R., Loranty, M. M., Macias-Fauria, M., Maseyk, K., Normand, S., Olofsson, J., Parker, T. C.,
Parmentier, F.-J. W., Post, E., Schaepman-Strub, G., Stordal, F., Sullivan, P. F., Thomas, H. J. D., Tømmervik, H., Treharne, R., Tweedie, C. E.,
Walker, D. A., Wilmking, M., and Wipf, S.: Complexity revealed in the
greening of the Arctic, Nat. Clim. Change, 10, 106–117, https://doi.org/10.1038/s41558-019-0688-1, 2020.
O’Donnell, M. S. and Ignizio, D. A.: Bioclimatic predictors for supporting ecological applications in the conterminous United States, US Geological Survey, Virginia, 2012.
Oksanen, L.: Isolated occurrences of spruce, Picea abies, in northernmost
Fennoscandia in relation to the enigma of continental mountain birch
forests, Acta Bot. Fenn., 153, 81–92, 1995.
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., and Wagner, H.: Community Ecology Package, Retrieved from: https://CRAN.R-project.org/package=vegan, last access: 3 April 2019.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M.,
Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
and Thornton, P. E.: Technical Description of version 4.5 of the Community
Land Model (CLM), NCAR Earth System Laboratory Climate and Global Dynamics
Division, Boulder, Colorado, USA, 2013.
Pebesma, E. J. and Bivand, R. S.: Classes and methods for spatial data in R, retrieved from: https://CRAN.R-project.org/doc/Rnews/ (last access: 4 March 2019), 2005.
Porada, P., Ekici, A., and Beer, C.: Effects of bryophyte and lichen cover on permafrost soil temperature at large scale, The Cryosphere, 10, 2291–2315, https://doi.org/10.5194/tc-10-2291-2016, 2016.
Poulter, B., Ciais, P., Hodson, E., Lischke, H., Maignan, F., Plummer, S., and Zimmermann, N. E.: Plant functional type mapping for earth system models, Geosci. Model Dev., 4, 993–1010, https://doi.org/10.5194/gmd-4-993-2011, 2011.
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R., Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., OttlÉ, C., Peters, M., and Peylin, P.: Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015.
QGIS Development Team: QGIS geographic information system: Open Source Geospatial Foundation Project, retrieved from: http://qgis.osgeo.org, last access: 1 March 2019.
R Core Team: R: A language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical Computing, retrieved from: https://www.R-project.org/, last access: 1 April 2019.
Rowlingson, B., Bivand, R., and Keitt, T.: Bindings for the “Geospatial” Data Abstraction Library, retrieved from: https://CRAN.R-project.org/package=rgdal, last access: 1 March 2019.
Scheiter, S., Langan, L., and Higgins, S. I.: Next-generation dynamic global
vegetation models: learning from community ecology, New Phytol., 198,
957–969, https://doi.org/10.1111/nph.12210, 2013.
Seo, H. and Kim, Y.: Interactive impacts of fire and vegetation dynamics on global carbon and water budget using Community Land Model version 4.5, Geosci. Model Dev., 12, 457–472, https://doi.org/10.5194/gmd-12-457-2019, 2019.
Sevanto, S., Suni, T., Pumpanen, J., Grönholm, T., Kolari, P., Nikinmaa, E., Hari, P., and Vesala, T.: Wintertime photosynthesis and water uptake in
a boreal forest, Tree Physiol., 26, 749–757, https://doi.org/10.1093/treephys/26.6.749, 2006.
Shi, Y., Yu, M., Erfanian, A., and Wang, G.: Modeling the Dynamic
Vegetation–Climate System over China Using a Coupled Regional Model, J.
Climate, 31, 6027–6049, https://doi.org/10.1175/jcli-d-17-0191.1, 2018.
Simensen, T., Horvath, P., Erikstad, L., Bryn, A., Vollering, J., and
Halvorsen, R.: Composite landscape predictors improve distribution models of
ecosystem types, Divers. Distrib., 26, 928–943, https://doi.org/10.1111/ddi.13060, 2020.
Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L.,
Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C. D., Prentice, I. C., and Woodward, F. I.: Evaluation of the terrestrial carbon cycle,
future plant geography and climate-carbon cycle feedbacks using five Dynamic
Global Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039,
https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008.
Snell, R. S., Huth, A., Nabel, J. E. M. S., Bocedi, G., Travis, J. M. J.,
Gravel, D., Bugmann, H., Gutiérrez, A. G., Hickler, T., Higgins, S. I.,
Reineking, B., Scherstjanoi, M., Zurbriggen, N., and Lischke, H.: Using
dynamic vegetation models to simulate plant range shifts, Ecography, 37,
1184–1197, https://doi.org/10.1111/ecog.00580, 2014.
Song, X., Zeng, X., and Zhu, J.: Evaluating the tree population density and
its impacts in CLM-DGVM, Adv. Atmos. Sci., 30, 116–124,
https://doi.org/10.1007/s00376-012-1271-0, 2013.
Strand, G.-H.: The Norwegian area frame survey of land cover and outfield
land resources, Norsk Geogr. Tidsskr., 67, 24–35, https://doi.org/10.1080/00291951.2012.760001, 2013.
Tang, H.: Modification and scripts for running CLM4.5BGCDV and sensitivity experiments, Zenodo, https://doi.org/10.5281/zenodo.4415469, 2021.
Ullerud, H. A., Bryn, A., and Klanderud, K.: Distribution modelling of
vegetation types in the boreal–alpine ecotone, Appl. Veg. Sci.,
19, 528–540, https://doi.org/10.1111/avsc.12236, 2016.
Ullerud, H. A., Bryn, A., and Skånes, H.: Bridging theory and
implementation – Testing an abstract classification system for practical
mapping by field survey and 3D aerial photographic interpretation, Norsk Geogr. Tidsskr., 73, 301–317, https://doi.org/10.1080/00291951.2020.1717595, 2020.
Vowles, T., Gunnarsson, B., Molau, U., Hickler, T., Klemedtsson, L., and
Björk, R. G.: Expansion of deciduous tall shrubs but not evergreen dwarf
shrubs inhibited by reindeer in Scandes mountain range, J. Ecol.,
105, 1547–1561, https://doi.org/10.1111/1365-2745.12753, 2017.
Wickham, H.: Elegant Graphics for Data Analysis, Springer-Verlag New York, retrieved from: https://ggplot2.tidyverse.org (last access: 5 April 2019), 2016.
Wullschleger, S. D., Epstein, H. E., Box, E. O., Euskirchen, E. S., Goswami, S., Iversen, C. M., Kattge, J., Norby, R. J., van Bodegom, P. M., and Xu, X.: Plant functional types in Earth system models: past experiences and
future directions for application of dynamic vegetation models in
high-latitude ecosystems, Ann. Bot.-London, 114, 1–16, https://doi.org/10.1093/aob/mcu077, 2014.
Xie, Y., Sha, Z., and Yu, M.: Remote sensing imagery in vegetation mapping:
a review, J. Plant Ecol., 1, 9–23, https://doi.org/10.1093/jpe/rtm005, 2008.
Zeng, X., Zeng, X., and Barlage, M.: Growing temperate shrubs over arid and
semiarid regions in the Community Land Model–Dynamic Global Vegetation
Model, Global Biogeochem. Cy., 22, GB3003, https://doi.org/10.1029/2007gb003014,
2008.
Zhang, W., Brandt, M., Tong, X., Tian, Q., and Fensholt, R.: Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel, Biogeosciences, 15, 319–330, https://doi.org/10.5194/bg-15-319-2018, 2018.
Zhu, J., Zeng, X., Zhang, M., Dai, Y., Ji, D., Li, F., Zhang, Q., Zhang, H.,
and Song, X.: Evaluation of the New Dynamic Global Vegetation Model in
CAS-ESM, Adv. Atmos. Sci., 35, 659–670, https://doi.org/10.1007/s00376-017-7154-7, 2018.
Zuur, A. F., Ieno, E. N., and Smith, G. M.: Measures of association, in:
Analysing ecological data, Statistics for Biology and Health, Springer, New
York, 163–187, 2007.
Short summary
We evaluated the performance of three methods for representing vegetation cover. Remote sensing provided the best match to a reference dataset, closely followed by distribution modelling (DM), whereas the dynamic global vegetation model (DGVM) in CLM4.5BGCDV deviated strongly from the reference. Sensitivity tests show that use of threshold values for predictors identified by DM may improve DGVM performance. The results highlight the potential of using DM in the development of DGVMs.
We evaluated the performance of three methods for representing vegetation cover. Remote sensing...
Altmetrics
Final-revised paper
Preprint