| 1. |
- Wu, Zhendong, et al.
(author)
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Approaching the potential of model-data comparisons of global land carbon storage
- 2019
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9
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Journal article (peer-reviewed)abstract
- Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.
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| 2. |
- Lehsten, Veiko, et al.
(author)
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Disentangling the effects of land-use change, climate and CO2 on projected future European habitat types
- 2015
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In: Global Ecology and Biogeography. - : Wiley. - 1466-8238 .- 1466-822X. ; 24:6, s. 653-663
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Journal article (peer-reviewed)abstract
- AimTo project the potential European distribution of seven broad habitat categories (needle-leaved, broad-leaved, mixed and mediterranean forest, urban, grassland and cropland) in order to assess effects of land use, climate change and increase in CO2 on predicted habitat changes up to the year 2050. LocationEurope. MethodWe modelled the response of European vegetation to changes in land use, climate and CO2 by combining the land-use model Dyna-CLUE (based on the CORINE land-cover data) and the dynamic vegetation model LPJ-GUESS. Two reforestation options were explored: maintaining the current range of tree species (EFI) or promoting naturally occurring tree species (NAT). Climate data from two general circulation models and two SRES scenarios (A2 and B1) were used. The broad habitat types were classified according to a combination of land use and the dominant plant species. ResultsOur models predicted that croplands and grasslands are expected to decrease due to land-use change. Although climate change has a negative effect on needle-leaved forest, it is expected to maintain its area or even increase in the EFI reforestation option while mediterranean, broad-leaved and mixed forests are expected to increase markedly. All investigated drivers have shown some effect, but land use is the dominant contributor to broad habitat change except for needle-leaved and mixed which are mainly influenced by climate change. Main conclusionsLand use is predicted to have the greatest effect on broad habitat distribution according to our simulations. Hence in most parts of Europe mitigating actions should focus on land-use change rather than climate change. According to our simulation, the effects of the different drivers are not in general additive. In some cases they act synergistically and in some cases antagonistically. The projected habitat changes are a valuable tool for species distribution modelling and are available online.
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| 3. |
- Akerman, H. Jonas, et al.
(author)
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Svalbards nya klimat
- 2023
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In: Det nya Svalbard. - 0044-0477. ; 143, s. 54-77
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Book chapter (other academic/artistic)
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| 4. |
- Blanke, Jan Hendrik, et al.
(author)
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Effect of climate data on simulated carbon and nitrogen balances for Europe
- 2016
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In: Journal of Geophysical Research - Biogeosciences. - 2169-8953. ; 121:5, s. 1352-1371
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Journal article (peer-reviewed)abstract
- In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%–93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%–10.7% of the total variability followed by GCMs (0.3%–7.6%), RCPs (0%–6.3%), and downscaling methods (0.1%–4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%–45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.
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| 5. |
- Boke-Olén, Niklas, et al.
(author)
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Analyzing savannah vegetation phenology with remotely sensed data, lagged time-series models and phenopictures
- 2016
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Conference paper (peer-reviewed)abstract
- It is predicted that savannah regions will see changes in precipitation patterns due to current climate change pro-jections. The change will most likely affect leaf phenology which controls net primary production. It is thereforeimportant to; 1) study those changes and its drivers, 2) to be able to correctly model the changes to vegetationphenology due to climate change. To our knowledge there is no existing global savannah phenology model thatcan capture both the phenological events and the vegetation state between the events. We therefore, investigate howday length, mean annual precipitation and soil moisture affects and controls the vegetation phenology of savannahs(using MODIS NDVI as a proxy for phenological state) with a lagged time series model for global application. Wefurthermore use phenological pictures (phenopictures) to investigate savannah tree and grass phenology. Phenopic-tures are pictures taken with a digital time-lapse camera with the purpose of recording and studying phenologicalevents. We used climate data from 15 flux towers sites located in 4 continents together with normalized differencevegetation index from MODIS for the model development. Two of the sites located in Africa were further ana-lyzed using phenopictures. The developed model identified all three considered variables as usable for modellingof savannah leaf phenology but showed some inconsistent result for some of the sites indicating the difficultiesin creating a simple common model that works equally well across sites. We attribute some of these difficultiesto site specific differences (e.g. grazing or tree and grass ratio) that the simplified model did not consider. Butwe expect it to on average give the cross-validated result (r2= 0.6, RMSE = 0.1) when applied to other savannahareas. The preliminary analysis of the phenological pictures with respect to tree and grass to some extent supportthis by showing differences in the start of the leaves development in the beginning of the season. However, thisdiffered between the two studied sites which further highlights the difficulties in creating a common model thatworks equally well for individual sites.
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| 8. |
- Molinari, Chiara, et al.
(author)
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The climate, the fuel and the land use: long-term regional variability of biomass burning in boreal forests
- 2018
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In: Global Change Biology. - : Wiley. - 1365-2486 .- 1354-1013. ; 24:10, s. 4929-4945
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Journal article (peer-reviewed)abstract
- The influence of different drivers on changes in North American and European boreal forests biomass burning (BB) during the Holocene was investigated based on the following hypotheses: land use was important only in the southernmost regions, while elsewhere climate was the main driver modulated by changes in fuel type. BB was reconstructed by means of 88 sedimentary charcoal records divided into six different site clusters. A statistical approach was used to explore the relative contribution of (a) pollen‐based mean July/summer temperature and mean annual precipitation reconstructions, (b) an independent model‐based scenario of past land use (LU), and (c) pollen‐based reconstructions of plant functional types (PFTs) on BB. Our hypotheses were tested with: (a) a west‐east northern boreal sector with changing climatic conditions and a homogeneous vegetation, and (b) a north‐south European boreal sector characterized by gradual variation in both climate and vegetation composition. The processes driving BB in boreal forests varied from one region to another during the Holocene. However, general trends in boreal biomass burning were primarily controlled by changes in climate (mean annual precipitation in Alaska, northern Quebec, and northern Fennoscandia, and mean July/summer temperature in central Canada and central Fennoscandia) and, secondarily, by fuel composition (BB positively correlated with the presence of boreal needleleaf evergreentrees in Alaska and in central and southern Fennoscandia). Land use playedonly a marginal role. A modification towards less flammable tree species (by promoting deciduous stands over fire‐prone conifers) could contribute to reduce circumboreal wildfire risk in future warmer periods.
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| 9. |
- Olén, Niklas Boke, et al.
(author)
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High-resolution global population projections dataset developed with CMIP6 RCP and SSP scenarios for year 2010-2100
- 2022
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In: Data in Brief. - : Elsevier BV. - 2352-3409. ; 40
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Journal article (peer-reviewed)abstract
- We present a novel, global 30 arc seconds (similar to 1 km at the equator) population projection dataset covering each year from 2010 to 2100 that is consistent with both country level population and gridded urban fractions from the Coupled Model Intercomparison Project 6 (CMIP6). While IPCC population projections until 2100 are available at country level for Socio-Economic Pathways (SSPs), land cover (including the urban fraction) is only available for Representative Concentration Pathways (RCPs). To perform simulations of e.g., future supply and demand for agricultural products, fine scale projections of population density are needed for combinations of SSPs and RCPs. Therefore, we generated a 30 arc seconds dataset consistent with both SSPs and RCPs within the framework of the IPCC. This data set is useful in applications where spatially explicit projections of aspects of global change are investigated at a fine spatial scale. For example, if a link function between night-time lights and population density is found based on current satellite images and recent population density data, a projection of night-time light lights can be generated by using this link function with our projected population density. Such a projection can for example be used to evaluate the potential for future light pollution.
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| 10. |
- Wu, Zhendong, et al.
(author)
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Climate data induced uncertainty in model-based estimations of terrestrial primary productivity
- 2017
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In: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 12:6
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Journal article (peer-reviewed)abstract
- Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9 % of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32 % of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.
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