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| 2. |
- Sathyanadh, Anusha, et al.
(författare)
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Reconciling the Carbon Balance of Northern Sweden Through Integration of Observations and Modelling
- 2021
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Ingår i: Journal of Geophysical Research: Atmospheres. - 2169-897X .- 2169-8996. ; 126:23
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Tidskriftsartikel (refereegranskat)abstract
- The boreal biome plays an important role in the global carbon cycle. However, current estimates of its sink-source strength and responses to changes in climate are primarily derived from models and thus remain uncertain. A major challenge is the validation of these models at a regional scale since empirical flux estimates are typically confined to ecosystem or continental scales. The Integrated Carbon Observation System (ICOS)-Svartberget atmospheric station (SVB) provides observations including tall tower eddy covariance (EC) and atmospheric concentration measurements that can contribute to such validation in Northern Sweden. Thus, the overall aim of this study was to quantify the carbon balance in Northern Sweden region by integrating land-atmosphere fluxes and atmospheric carbon dioxide (CO2) concentrations. There were three specific objectives. First, to compare flux estimates from four models (VPRM, LPJ-GUESS, ORCHIDEE, and SiBCASA) to tall tower EC measurements at SVB during the years 2016–2018. Second to assess the fluxes' impact on atmospheric CO2 concentrations using a regional transport model. Third, to assess the impact of the drought in 2018. The comparison of estimated concentrations with ICOS observations helped the evaluation of the models' regional scale performance. Both the simulations and observations indicate there were similar reductions in the net CO2 uptake during drought. All the models (except for SiBCASA) and observations indicated the region was a net carbon sink during the 3-year study period. Our study highlights a need to improve vegetation models through comparisons with empirical data and demonstrate the ICOS network's potential utility for constraining CO2 fluxes in the region.
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| 3. |
- Tagesson, Torbern, et al.
(författare)
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Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink
- 2020
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Ingår i: Nature Ecology and Evolution. - : Springer Science and Business Media LLC. - 2397-334X. ; 4, s. 202-209
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Tidskriftsartikel (refereegranskat)abstract
- Anthropogenic land use and land cover changes (LULCC) have a large impact on the global terrestrial carbon sink, but this effect is not well characterized according to biogeographical region. Here, using state-of-the-art Earth observation data and a dynamic global vegetation model, we estimate the impact of LULCC on the contribution of biomes to the terrestrial carbon sink between 1992 and 2015. Tropical and boreal forests contributed equally, and with the largest share of the mean global terrestrial carbon sink. CO2 fertilization was found to be the main driver increasing the terrestrial carbon sink from 1992 to 2015, but the net effect of all drivers (CO2 fertilization and nitrogen deposition, LULCC and meteorological forcing) caused a reduction and an increase, respectively, in the terrestrial carbon sink for tropical and boreal forests. These diverging trends were not observed when applying a conventional LULCC dataset, but were also evident in satellite passive microwave estimates of aboveground biomass. These datasets thereby converge on the conclusion that LULCC have had a greater impact on tropical forests than previously estimated, causing an increase and decrease of the contributions of boreal and tropical forests, respectively, to the growing terrestrial carbon sink.
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| 5. |
- Wang, Lanhui, et al.
(författare)
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Asymmetric patterns and temporal changes in phenology-based seasonal gross carbon uptake of global terrestrial ecosystems
- 2020
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Ingår i: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 29:6, s. 1020-1033
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To study global patterns and temporal changes in the seasonal dynamics (quantity and seasonal distribution) of terrestrial gross carbon uptake in response to global environmental change. Location: Global. Time period: 2000–2016. Major taxa studied: Terrestrial ecosystems. Methods: Following a phenology-based definition of photosynthetic seasonality, we decompose gross primary production (GPP) into three periods, green-up, maturity and senescence, and derive their corresponding GPP (GPPgp, GPPmp and GPPsp, respectively) from a newly developed time series of satellite-based global GPP to study spatio-temporal dynamics of seasonal GPP. Results: We find that the global fraction of GPPsp (19.8%) is larger than GPPgp (14.3%), indicating a globally asymmetric seasonal distribution of gross carbon uptake by terrestrial ecosystems. Globally, GPPmp plays a dominant role in shaping spatial patterns and increasing/decreasing trends in GPP, while GPPgp/GPPsp contributes to increasing GPP at the regional scale. Higher fractions of GPPgp/GPPmp (lower of GPPsp), as well as the co-occurrence of increasing GPP and non-tree vegetation cover in major croplands, are likely to be caused by agricultural intensification. Global changes in GPPgp and GPPsp are closely related to changes in their seasonal distributions (R =.86/.8, respectively), whereas this relationship is weaker for GPPmp (R =.53). Finally, high correlations are observed between changes in GPPgp and GPPsp and changes in their durations (R =.78/.78, respectively), while GPPmp shows a relatively lower correlation with its duration (R =.67). Main conclusions: The asymmetric spatio-temporal patterns in the seasonal dynamics of global terrestrial gross carbon uptake found here have been substantially reshaped by anthropogenic land-use/cover changes and changes in photosynthetic phenology. Compared to calendar-based meteorological seasons more suitable for temperate/subpolar ecosystems, our phenology-based approach is expected to provide an alternative starting point for a better understanding of global spatio-temporal changes in the seasonal dynamics of terrestrial ecosystem processes and functioning under accelerating global change.
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| 6. |
- Wang, Zhendong, et al.
(författare)
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Parallel Multigrid for Nonlinear Cloth Simulation
- 2018
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Ingår i: Computer Graphics Forum. - : Wiley. - 1467-8659 .- 0167-7055. ; 37:7, s. 131-141
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Tidskriftsartikel (refereegranskat)abstract
- Accurate high-resolution simulation of cloth is a highly desired computational tool in graphics applications. As single-resolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi-resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high-resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton's method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi-resolution collision handling for real-time simulation in the future.
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| 7. |
- Wu, Zhendong, et al.
(författare)
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Approaching the potential of model-data comparisons of global land carbon storage
- 2019
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9
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Tidskriftsartikel (refereegranskat)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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| 8. |
- Wu, Zhendong, et al.
(författare)
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Climate data induced uncertainty in model-based estimations of terrestrial primary productivity
- 2017
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Ingår i: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 12:6
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Wu, Zhendong, et al.
(författare)
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Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions
- 2018
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 13:6
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Tidskriftsartikel (refereegranskat)abstract
- Biogeochemical models use meteorological forcing data derived with different approaches(e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulateecosystem processes such as gross primary productivity (GPP). This study assesses theimpact of different widely used climate datasets on simulated gross primary productivity andevaluates the suitability of them for reproducing the global and regional carbon cycle asmapped from independent GPP data. We simulate GPP with the biogeochemical modelLPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP,PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-basedGPP product derived from eddy covariance measurements in combination with remotelysensed data. Our results show that all datasets tested produce relatively similar GPP simulationsat a global scale, corresponding fairly well to the observation-based data with a differencebetween simulations and observations ranging from -50 to 60 g m-2 yr-1. However, allsimulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1)and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwaveradiation for tropical areas was found to have the highest uncertainty in the analyzed historicalclimate datasets, we test whether simulation results could be improved by a correction ofthe tested shortwave radiation for tropical areas using a new radiation product from the InternationalSatellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) insimulated GPP magnitude was observed with bias corrected shortwave radiation, as well asan increase in spatio-temporal agreement between the simulated GPP and observationbasedGPP. This study conducts a spatial inter-comparison and quantification of the performancesof climate datasets and can thereby facilitate the selection of climate forcing dataover any given study area for modelling purposes.
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| 10. |
- Wu, Zhendong
(författare)
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Modelling the terrestrial carbon cycle – drivers, benchmarks, and model-data fusion
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The terrestrial ecosystem sequesters about one-third of anthropogenic emissions each year, thereby providing a critical ecosystem service that slows the rate of increase of atmospheric carbon dioxide and helps mitigate climate change. Observed atmospheric carbon dioxide concentrations exhibit a large inter-annual variability which is considered to be caused primarily by the response of the terrestrial ecosystem to climate change and anthropogenic activity. A better understanding of the functioning of the terrestrial ecosystem is therefore required to improve our ability to predict the global carbon cycle and climate change.Ecosystem models integrate and apply knowledge of ecological processes (e.g. photosynthesis, respiration, allocation, and other plant physiological and microbial processes) to simulate net primary production, biomass accumulation, litterfall and soil carbon amongst others, in terrestrial ecosystems worldwide. These models are widely applied to explore, analyze and further our understanding of the complex interactions among biomes as well as the flows of carbon, nutrients and water through ecosystems over time in response to climate change and disturbances. Ecosystem models also allow the projection of the evolution of the carbon cycle under different scenarios of future possible carbon dioxide concentrations. However, current studies have demonstrated large uncertainties in predictions of past and present terrestrial carbon dynamics which limits our confidence in projections of future changes. These uncertainties, originating from model structure, parameters and data that drives the model, greatly limits our ability to accurately assess the performance of ecosystem models as well as our understanding of the response of ecosystems to environmental changes.This thesis aims to analyze these caveats by disentangling the causes of uncertainties in modeling terrestrial carbon dynamics to inform future model improvement. A state-of-the-art ecosystem model LPJ-GUESS is employed as the model platform for this study. Climate data induced uncertainty in model-based estimations of terrestrial primary productivity are analyzed and quantified for different ecosystems. Also, different climate variables are identified as the main contributors to total climate induced uncertainty in different regions. In addition, this thesis assesses the suitability of contemporary climate datasets with respect to a given research purpose and study area, and quantifies the effect of land use and land cover changes on the terrestrial carbon sink. Moreover, a matrix approach, which reorganizes the carbon balance equations of the ecosystem models into one matrix equation while preserving dynamically modeled carbon cycle processes and mechanisms, is applied to identify which ecological processes contribute most strongly to model-data disagreement in term of terrestrial carbon storage and flux.Identifying and reducing uncertainty in estimations of the terrestrial carbon cycle via a modeling approach enables us better understand, quantify, and forecast the effects of climate change and anthropogenic activity on the terrestrial ecosystem, but is also of increasing relevance in the context of climate change mitigation policies.
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