Recently, nanocarriers that transport bioactive substances to a target site in the body have attracted considerable attention and undergone rapid progression in terms of the state of the art. ...However, few nanocarriers can enter the brain via a systemic route through the blood-brain barrier (BBB) to efficiently reach neurons. Here we prepare a self-assembled supramolecular nanocarrier with a surface featuring properly configured glucose. The BBB crossing and brain accumulation of this nanocarrier are boosted by the rapid glycaemic increase after fasting and by the putative phenomenon of the highly expressed glucose transporter-1 (GLUT1) in brain capillary endothelial cells migrating from the luminal to the abluminal plasma membrane. The precisely controlled glucose density on the surface of the nanocarrier enables the regulation of its distribution within the brain, and thus is successfully optimized to increase the number of nanocarriers accumulating in neurons.There are only a few examples of nanocarriers that can transport bioactive substances across the blood-brain barrier. Here the authors show that by rapid glycaemic increase the accumulation of a glucosylated nanocarrier in the brain can be controlled.
The flux of carbon dioxide from the soil to the atmosphere (soil respiration) is one of the major fluxes in the global carbon cycle. At present, the accumulated field observation data cover a wide ...range of geographical locations and climate conditions. However, there are still large uncertainties in the magnitude and spatiotemporal variation of global soil respiration. Using a global soil respiration data set, we developed a climate-driven model of soil respiration by modifying and updating Raich's model, and the global spatiotemporal distribution of soil respiration was examined using this model. The model was applied at a spatial resolution of 0.5°and a monthly time step. Soil respiration was divided into the heterotrophic and autotrophic components of respiration using an empirical model. The estimated mean annual global soil respiration was 91 Pg C yr−1 (between 1965 and 2012; Monte Carlo 95 % confidence interval: 87–95 Pg C yr−1) and increased at the rate of 0.09 Pg C yr−2. The contribution of soil respiration from boreal regions to the total increase in global soil respiration was on the same order of magnitude as that of tropical and temperate regions, despite a lower absolute magnitude of soil respiration in boreal regions. The estimated annual global heterotrophic respiration and global autotrophic respiration were 51 and 40 Pg C yr−1, respectively. The global soil respiration responded to the increase in air temperature at the rate of 3.3 Pg C yr−1 °C−1, and Q10 = 1.4. Our study scaled up observed soil respiration values from field measurements to estimate global soil respiration and provide a data-oriented estimate of global soil respiration. The estimates are based on a semi-empirical model parameterized with over one thousand data points. Our analysis indicates that the climate controls on soil respiration may translate into an increasing trend in global soil respiration and our analysis emphasizes the relevance of the soil carbon flux from soil to the atmosphere in response to climate change. Further approaches should additionally focus on climate controls in soil respiration in combination with changes in vegetation dynamics and soil carbon stocks, along with their effects on the long temporal dynamics of soil respiration. We expect that these spatiotemporal estimates will provide a benchmark for future studies and also help to constrain process-oriented models.
Calibration of global hydrological models (GHMs) has been attempted for over two decades; however, an effective and generic calibration method has not been explored. We present a novel framework for ...calibrating GHMs assuming that parameters can be regionalized by climate similarities. We calibrated four sensitive parameters of the H08 global hydrological model by aggregating the results of 5,000 simulations with randomly generated parameters into 11 Köppen climate classes and using an objective function Nash–Sutcliffe Efficiency (NSE) with random sampling from the proposed parameter distribution. From a 100‐fold split‐sampling test, we found that both the representativeness and robustness of the transferred parameter sets were guaranteed when the upper 5% of the samples were accepted and assign the median of each accepted parameter distribution for the climate class. The simulation with the climate‐based parameters yielded satisfactory (NSE > 0.0) and good (NSE > 0.5) performances at 480 and 234 stations (61.7% and 30.1% of 777 stations), respectively. The storage capacity (SD) and the conductive coefficient (CD) were sensitive to the climate classes and exhibited well‐constrained distributions of the accepted samples, whereas the recession parameters for the subsurface storage (γ and τ) showed little or no explanatory power to climate. The identified parameters for climate classes exhibited consistency with the physical interpretation of soil formation and efficiencies in vapor transfer. The consistency of the identified parameter values with physical underpinnings indicates that the appropriate parameters were determined, which ensured the robustness of parameters, especially when they are transferred to ungauged watersheds.
Key Points
We tested the hypothesis that the climate properties exert a dominant influence on parameter similarities at the global scale
The simulation with the climate‐based parameters yielded improvement from default parameters
Appropriate parameter values were determined, and results demonstrate their robustness, especially when applied to ungauged watersheds
Abstract
East Asia is the one of the hotspot regions with too much reactive nitrogen (N) inputs from anthropogenic sources. Here, we evaluated historical total inorganic N (TIN) load from land to sea ...through the rivers surrounding the East China sea using biogeochemical model ‘VISIT’ combined with a newly developed VISIT Off-line River Nitrogen scheme (VISIToRN). VISIT calculated N cycling in both natural and agricultural ecosystems and VISIToRN calculated inorganic N transport and riverine denitrification through the river channels at half degree spatial resolution. Between 1961 and 2010, the estimated TIN load from land to the sea surrounding the East China Sea increased from 2.7 Tg-N Year
−1
to 5.5 Tg-N Year
−1
, a twofold increase, while the anthropogenic N input to the East China Sea basin (N deposition, N fertilizer, manure, and human sewage) increased from 12.9 Tg-N Year
−1
to 36.9 Tg-N Year
−1
, an increase of about 3 times. This difference in the rate of increase is due in large part to the terrestrial nitrogen budget, and the results of the model balance indicate that TIN load to rivers has been suppressed by improvements in fertilizer application rates, harvesting on agricultural land, and nitrogen accumulation in forests. The results of the model balance showed that the increase rate of nitrogen runoff from Chinese rivers has been declining since 2000. In our estimation by VISIToRN, the amount of nitrogen removed by river denitrification in the river channel before the mouth is not negligible, ranging from 1.6 Tg-N Year
−1
to 2.16 Tg-N Year
−1
. The N load from agricultural sources is still significant and needs to be further reduced. TIN load tended to increase in years with high precipitation. In order to effectively reduce TIN load, it is necessary to consider climate change-adaptive agricultural N management.
The tundra ecosystem is quite vulnerable to drastic climate change in the Arctic, and the quantification of carbon dynamics is of significant importance regarding thawing permafrost, changes to the ...snow-covered period and snow and shrub community extent, and the decline of sea ice in the Arctic. Here, CO2 efflux measurements using a manual chamber system within a 40 m × 40 m (5 m interval; 81 total points) plot were conducted within dominant tundra vegetation on the Seward Peninsula of Alaska, during the growing seasons of 2011 and 2012, for the assessment of driving parameters of CO2 efflux. We applied a hierarchical Bayesian (HB) model - a function of soil temperature, soil moisture, vegetation type, and thaw depth - to quantify the effects of environmental factors on CO2 efflux and to estimate growing season CO2 emissions. Our results showed that average CO2 efflux in 2011 was 1.4 times higher than in 2012, resulting from the distinct difference in soil moisture between the 2 years. Tussock-dominated CO2 efflux is 1.4 to 2.3 times higher than those measured in lichen and moss communities, revealing tussock as a significant CO2 source in the Arctic, with a wide area distribution on the circumpolar scale. CO2 efflux followed soil temperature nearly exponentially from both the observed data and the posterior medians of the HB model. This reveals that soil temperature regulates the seasonal variation of CO2 efflux and that soil moisture contributes to the interannual variation of CO2 efflux for the two growing seasons in question. Obvious changes in soil moisture during the growing seasons of 2011 and 2012 resulted in an explicit difference between CO2 effluxes - 742 and 539 g CO2 m-2 period-1 for 2011 and 2012, respectively, suggesting the 2012 CO2 emission rate was reduced to 27% (95% credible interval: 17-36%) of the 2011 emission, due to higher soil moisture from severe rain. The estimated growing season CO2 emission rate ranged from 0.86 Mg CO2 in 2012 to 1.20 Mg CO2 in 2011 within a 40 m × 40 m plot, corresponding to 86 and 80% of annual CO2 emission rates within the western Alaska tundra ecosystem, estimated from the temperature dependence of CO2 efflux. Therefore, this HB model can be readily applied to observed CO2 efflux, as it demands only four environmental factors and can also be effective for quantitatively assessing the driving parameters of CO2 efflux.
It is now widely recognized that climate change affects multiple sectors in virtually every part of the world. Impacts on one sector may influence other sectors, including seemingly remote ones, ...which we call “interconnections of climate risks.” While a substantial number of climate risks are identified in the Intergovernmental Panel on Climate Change Fifth Assessment Report, there have been few attempts to explore the interconnections between them in a comprehensive way. To fill this gap, we developed a methodology for visualizing climate risks and their interconnections based on a literature survey. Our visualizations highlight the need to address climate risk interconnections in impact and vulnerability studies. Our risk maps and flowcharts show how changes in climate impact natural and socioeconomic systems, ultimately affecting human security, health, and well‐being. We tested our visualization approach with potential users and identified likely benefits and issues. Our methodology can be used as a communication tool to inform decision makers, stakeholders, and the general public of the cascading risks that can be triggered by climate change.
Plain Language Summary
The paper demonstrates in a most holistic manner how climate change can generate various risks and how they are actually interconnected. Based on a literature survey using the Intergovernmental Panel on Climate Change Fifth Assessment Report, we identified 91 climate risks and 253 causal relationships among them and graphically drew such interconnected risks. We found that changes in the climate system impact the natural and socioeconomic system, influencing ultimately human security, health, and well‐being. This indicates that climate change can trigger a cascade of impacts across sectors. Our findings point to the need to address the climate risk interconnections in impact and vulnerability studies. We tested our visualization approach with potential users and identified likely benefits and issues. The implications of our study go beyond science. Our study is useful to inform stakeholders of a broad yet fresh perspective of climate risks that have not been presented before.
Key Points
The paper developed a methodology for visualizing how climate change can generate various risks and how they can be interconnected
We identified 91 climate risks and 253 causal relationships among them based on a literature survey and graphically presented the interconnected risks
We found that changes in the climate system impact natural and socioeconomic systems, ultimately influencing human security, health, and well‐being
Future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) ...simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed for differences between impact models. Projections of change from a baseline period (1981–2010) to the future (2070–2099) from 12 impacts models which contributed to the hydrological and biomes sectors of ISI-MIP were studied. The biome models differed from the hydrological models by the inclusion of CO2 impacts and most also included a dynamic vegetation distribution. The biome and hydrological models agreed on the sign of runoff change for most regions of the world. However, in West Africa, the hydrological models projected drying, and the biome models a moistening. The biome models tended to produce larger increases and smaller decreases in regionally averaged runoff than the hydrological models, although there is large inter-model spread. The timing of runoff change was similar, but there were differences in magnitude, particularly at peak runoff. The impact of vegetation distribution change was much smaller than the projected change over time, while elevated CO2 had an effect as large as the magnitude of change over time projected by some models in some regions. The effect of CO2 on runoff was not consistent across the models, with two models showing increases and two decreases. There was also more spread in projections from the runs with elevated CO2 than with constant CO2. The biome models which gave increased runoff from elevated CO2 were also those which differed most from the hydrological models. Spatially, regions with most difference between model types tended to be projected to have most effect from elevated CO2, and seasonal differences were also similar, so elevated CO2 can partly explain the differences between hydrological and biome model runoff change projections. Therefore, this shows that a range of impact models should be considered to give the full range of uncertainty in impacts studies.
We examined the changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVMs) obtained from the ...Inter-Sectoral Impact Model Intercomparison Project. Simulation results were obtained using five global climate models (GCMs) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (i.e., emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. At the end of the simulation period, changes from the year 2000 in all three variables varied considerably from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC and SOC projections, GVMs are the main influence on uncertainties (60 % and 90 %, respectively) rather than climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in vegetation carbon residence times is dominated by GVM uncertainty, particularly in the latter half of the 21st century. In addition, we found that the contribution of each uncertainty source is spatiotemporally heterogeneous and it differs among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division becomes cooler (from ca. 80 % in the equatorial division to 40 % in the snow division). Our results suggest that to assess climate change impacts on global ecosystem C cycling among each RCP scenario, the long-term C dynamics within the ecosystems (i.e., vegetation turnover and soil decomposition) are more critical factors than photosynthetic processes. The different trends in the contribution of uncertainty sources in each variable among climate divisions indicate that improvement of GVMs based on climate division or biome type will be effective. On the other hand, in dry regions, GCMs are the dominant uncertainty source in climate impact assessments of vegetation and soil C dynamics.
Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and may play a key role in biospheric feedbacks with elevated atmospheric carbon dioxide (CO2) in a warmer future world. ...We examined the simulation results of seven terrestrial biome models when forced with climate projections from four representative-concentration-pathways (RCPs)-based atmospheric concentration scenarios. The goal was to specify calculated uncertainty in global SOC stock projections from global and regional perspectives and give insight to the improvement of SOC-relevant processes in biome models. SOC stocks among the biome models varied from 1090 to 2650 Pg C even in historical periods (ca. 2000). In a higher forcing scenario (i.e., RCP8.5), inconsistent estimates of impact on the total SOC (2099–2000) were obtained from different biome model simulations, ranging from a net sink of 347 Pg C to a net source of 122 Pg C. In all models, the increasing atmospheric CO2 concentration in the RCP8.5 scenario considerably contributed to carbon accumulation in SOC. However, magnitudes varied from 93 to 264 Pg C by the end of the 21st century across biome models. Using the time-series data of total global SOC simulated by each biome model, we analyzed the sensitivity of the global SOC stock to global mean temperature and global precipitation anomalies (ΔT and ΔP respectively) in each biome model using a state-space model. This analysis suggests that ΔT explained global SOC stock changes in most models with a resolution of 1–2 °C, and the magnitude of global SOC decomposition from a 2 °C rise ranged from almost 0 to 3.53 Pg C yr−1 among the biome models. However, ΔP had a negligible impact on change in the global SOC changes. Spatial heterogeneity was evident and inconsistent among the biome models, especially in boreal to arctic regions. Our study reveals considerable climate uncertainty in SOC decomposition responses to climate and CO2 change among biome models. Further research is required to improve our ability to estimate biospheric feedbacks through both SOC-relevant and vegetation-relevant processes.
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