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- Zhou, Huimin, et al.
(author)
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Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands
- 2021
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In: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 307
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Journal article (peer-reviewed)abstract
- Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.
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2. |
- Flores, Samuel, et al.
(author)
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Multiscale modeling of macromolecular biosystems
- 2012
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In: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 13:4, s. 395-405
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Journal article (peer-reviewed)abstract
- In this article, we review the recent progress in multiresolution modeling of structure and dynamics of protein, RNA and their complexes. Many approaches using both physics-based and knowledge-based potentials have been developed at multiple granularities to model both protein and RNA. Coarse graining can be achieved not only in the length, but also in the time domain using discrete time and discrete state kinetic network models. Models with different resolutions can be combined either in a sequential or parallel fashion. Similarly, the modeling of assemblies is also often achieved using multiple granularities.The progress shows that a multiresolution approach has considerable potential to continue extending the length and time scales of macromolecular modeling.
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3. |
- Han, Jingrui, et al.
(author)
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Lattice Oxygen Activation through Deep Oxidation of Co4N by Jahn–Teller–Active Dopants for Improved Electrocatalytic Oxygen Evolution
- 2024
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In: Angewandte Chemie International Edition. - : John Wiley & Sons. - 1433-7851 .- 1521-3773. ; 63:33
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Journal article (peer-reviewed)abstract
- Triggering the lattice oxygen oxidation mechanism is crucial for improving oxygen evolution reaction (OER) performance, because it could bypass the scaling relation limitation associated with the conventional adsorbate evolution mechanism through the direct formation of oxygen–oxygen bond. High-valence transition metal sites are favorable for activating the lattice oxygen, but the deep oxidation of pre-catalysts suffers from a high thermodynamic barrier. Here, taking advantage of the Jahn–Teller (J–T) distortion induced structural instability, we incorporate high-spin Mn3+ ( ) dopant into Co4N. Mn dopants enable a surface structural transformation from Co4N to CoOOH, and finally to CoO2, as observed by various in situ spectroscopic investigations. Furthermore, the reconstructed surface on Mn-doped Co4N triggers the lattice oxygen activation, as evidenced experimentally by pH-dependent OER, tetramethylammonium cation adsorption and online electrochemical mass spectrometry measurements of 18O-labelled catalysts. In general, this work not only offers the introducing J–T effect approach to regulate the structural transition, but also provides an understanding about the influence of the catalyst's electronic configuration on determining the reaction route, which may inspire the design of more efficient catalysts with activated lattice oxygen.
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4. |
- Keel, Sonja G., et al.
(author)
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Allocation of carbon to fine root compounds and their residence times in a boreal forest depend on root size class and season
- 2012
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In: New Phytologist. - Malden : Wiley-Blackwell. - 0028-646X .- 1469-8137. ; 194:4, s. 972-981
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Journal article (peer-reviewed)abstract
- Fine roots play a key role in the forest carbon balance, but their carbon dynamics remain largely unknown. We pulse labelled 50 m2 patches of young boreal forest by exposure to 13CO2 in early and late summer. Labelled photosynthates were traced into carbon compounds of < 1 and 13 mm diameter roots (fine roots), and into bulk tissue of these and first-order roots (root tips). Root tips were the most strongly labelled size class. Carbon allocation to all size classes was higher in late than in early summer; mean residence times (MRTs) in starch increased from 4 to 11 months. In structural compounds, MRTs were 0.8 yr in tips and 1.8 yr in fine roots. The MRT of carbon in sugars was in the range of days. Functional differences within the fine root population were indicated by carbon allocation patterns and residence times. Pronounced allocation of recent carbon and higher turnover rates in tips are associated with their role in nutrient and water acquisition. In fine roots, longer MRTs but high allocation to sugars and starch reflect their role in structural support and storage. Accounting for heterogeneity in carbon residence times will improve and most probably reduce the estimates of fine root production.
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5. |
- Niu, Shuli, et al.
(author)
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Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms.
- 2012
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In: New Phytologist. - : Wiley. - 1469-8137 .- 0028-646X. ; 194:3, s. 775-783
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Journal article (peer-reviewed)abstract
- • It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem-climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.
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6. |
- Wang, Xuhui, et al.
(author)
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Global irrigation contribution to wheat and maize yield
- 2021
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
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Journal article (peer-reviewed)abstract
- Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.
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7. |
- Wartenburger, Richard, et al.
(author)
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Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
- 2018
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In: Environmental Research Letters. - : IOP Publishing. - 1748-9326. ; 13:7
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Journal article (peer-reviewed)abstract
- Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
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