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Atmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further. Plain Language Summary: Atmospheric CO2 flux inversions rely heavily on assumptions about errors in the initial flux estimate. If these errors are related in time or in space, inversions need to represent both the shape and the size of the likely errors. Previous studies have looked at the shape of the errors in daily average fluxes, but fewer have looked at the relationship between errors in one part of a day and in another part of that day, or between 1 year and the next. The study examines relationships in differences between measured and computer‐predicted ecosystem‐atmosphere carbon dioxide fluxes. There are strong relationships between flux errors separated by multiple years. Current attempts to solve for true ecosystem‐atmosphere fluxes using atmospheric data do not take these multi‐year relationships into consideration. Accounting for the multi‐year relationships may improve our ability to determine the true ecosystem‐atmosphere CO2 fluxes. We look for descriptions of these relationships that researchers can use when using atmospheric data to improve the computer‐predicted CO2 fluxes. Key Points: CO2 flux model‐data differences have autocorrelations that recur at intervals of a year with little decay over multiple yearsThese autocorrelations are largely caused by errors in the representation of the daily cycleWe can treat these persistent errors in the daily cycle either as prior flux error correlations or by corrections to the prior flux model [ABSTRACT FROM AUTHOR]

In recent years, the range of laser applications in various fields is increasing in a visible speed. In particular, ultra‐short pulse laser, with its non‐contact, high precision, small thermal effect, high flexibility, in the field of fine processing, especially in the semiconductor electronics industry, is rapidly replacing the traditional processing methods. Mini‐LED is diode that is smaller than normal LED. Thanks to the smaller size of diodes, more leds can be combined to provide more discrete dimming areas. Mini‐LED has the characteristics of ultra‐ high brightness, ultra‐high contrast, ultra‐wide color gamut and extremely low frequency flash, which has great potential for future development. In the Mini‐LED industry, laser technology in cutting, drilling, etching, cleaning and other aspects have a very high application value. In this paper, the application direction of laser technology in Mini‐LED glass base will be discussed. [ABSTRACT FROM AUTHOR]

Background: Amide proton transfer (APT) imaging has been increasingly applied in tumor characterization. However, its value in evaluating breast cancer remains undetermined. Purpose: To assess the diagnostic performance of APT imaging in breast cancer and its association with prognostic histopathologic characteristics. Study Type: Prospective. Subjects: Eighty‐four patients with breast lesions. Field Strength/Sequence: A 3.0 T/single‐shot fast spin echo APT imaging. Assessment: APTw signal in breast lesion was quantified. Lesion malignancy, T stage, grades, Ki‐67 index, molecular biomarkers (estrogen receptor [ER] expression, progesterone receptor [PR] expression, human epidermal growth factor receptor [HER‐2] expression), molecular subtypes (luminal A, luminal B, triple negative, and HER‐2 enriched) were determined. Statistical Tests: Student t‐test, one‐way analysis of variance, receiver operating characteristic analysis, and Pearson's correlation with P < 0.05 as statistical significance. Results: APTw signal was significantly higher in malignant lesions (1.55% ± 1.24%) than in benign lesions (0.54% ± 1.13%), and in grade III lesions than in grade II lesions (1.65% ± 0.84% vs. 0.96% ± 0.96%), and in T2‐ (1.57% ± 0.64%) and T3‐stage lesions (1.54% ± 0.63%) than in T1‐stage lesions (0.81% ± 0.64%) for invasive breast carcinoma of no special type. APTw signal significantly correlated with Ki‐67 index (r = 0.364) but showed no significant difference in groups of ER (P = 0.069), PR (P = 0.069), HER‐2 (P = 0.961), and among molecular subtypes (P = 0.073). Data Conclusion: APT imaging shows potential in differentiating breast lesion malignancy and associates with prognosis‐related tumor grade, T stage, and proliferative activity. Evidence Level: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Frontal boundaries have been shown to cause large changes in CO2 mole‐fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole‐fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport‐America (ACT‐America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting‐—Chemistry (WRF‐Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF‐Chem for summer‐2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically‐active weather systems. Key Points: First airborne observations of column average CO2 dry‐air mole‐fraction (XCO2) changes across fronts observed during ACT‐A are reportedXCO2 frontal structures compare reasonably well with Weather Research and Forecasting—Chemistry and an in situ data driven assimilation systemResults reveal that the differences across models and data were generally much smaller than the magnitude of XCO2 frontal contrasts [ABSTRACT FROM AUTHOR]

We present observations of local enhancements in carbon dioxide (CO2) from local emissions sources over three eastern US regions during four deployments of the Atmospheric Carbon Transport‐America (ACT‐America) campaign between summer 2016 and spring 2018. Local CO2 emissions were characterized by carbon monoxide (CO) to CO2 enhancement ratios (i.e., ΔCO/ΔCO2) in air mass mixing observed during aircraft transects within the planetary boundary layer. By analyzing regional‐scale variability of CO2 enhancements as a function of ΔCO/ΔCO2 enhancement ratios, observed relative contributions to CO2 emissions were separated into fossil fuel and biomass burning (BB) regimes across regions and seasons. CO2 emission contributions attributed to biomass burning (ΔCO/ΔCO2 > 4%) were negligible during summer and fall in all regions but climbed to ∼9%–11% of observed combustion contributions in the South during winter and spring. Relative CO2 fire emission trends matched observed winter and spring BB contributions, but conflictingly predicted similar levels of BB during the fall. Satellite fire data from MODIS and VIIRS suggested the use of higher spatial resolution fire data that might improve modeled BB emissions but were not able to explain the bulk of the discrepancy. Key Points: Airborne CO and CO2 enhancement ratios used to examine distribution of CO2 emissions by combustion source efficiencyDiscrepancies observed between model and airborne results in seasonal and regional behavior of biomass: fossil fuel burning CO2 emission ratiosSatellite fire data suggest discrepancies may be partially due to mix of spatial resolution and biomass/fire parameterization [ABSTRACT FROM AUTHOR]

The Atmospheric Carbon Transport (ACT)‐America Earth Venture mission conducted five airborne campaigns across four seasons from 2016 to 2019, to study the transport and fluxes of Greenhouse gases across the eastern United States. Unprecedented spatial sampling of atmospheric tracers (CO2, carbon monoxide [CO], carbonyl sulfide [COS]) related to biospheric processes offers opportunities to improve our qualitative and quantitative understanding of seasonal and spatial patterns of biospheric carbon uptake. Here, we examine co‐variation of boundary layer enhancements of CO2, CO, and COS across three diverse regions: the crop‐dominated Midwest, evergreen‐dominated South, and deciduous broadleaf‐dominated Northeast. To understand the biogeochemical processes controlling these tracers, we compare the observed co‐variation to simulated co‐variation resulting from model‐ and satellite‐ constrained surface carbon fluxes. We found indication of a common terrestrial biogenic sink of CO2 and COS and secondary production of CO from biogenic sources in summer throughout the eastern US, driven by stomatal conductance. Upper Midwest crops drive ΔCO2 and ΔCOS depletion from early to late summer. Northeastern temperate forests drive ΔCO2 and ΔCOS depletion in late summer. The unprecedented ACT‐America flask samples uncovered evidence that southern humid temperate forests photosynthesize and absorb CO2 and COS, and emit CO precursors, deep into the growing season. Satellite‐ constrained carbon fluxes capture much of the observed seasonal and spatial variability, but underestimate the magnitude of net CO2 and COS depletion in the South, indicating a stronger than expected net sink of CO2 in late summer. Additional sampling of the South will more accurately constrain underlying biological processes and climate sensitivities governing southern carbon dynamics. Plain Language Summary: The Atmospheric Carbon Transport (ACT)‐America airborne mission provided unprecedented sampling of atmospheric greenhouse gas concentrations throughout the eastern United States from 2016 to 2019. A subset of these gases, namely carbon dioxide (CO2), carbonyl sulfide (COS), and carbon monoxide (CO), are strongly influenced by photosynthetic activity in plants. Unlike other sources of carbon such as fossil fuels and biomass burning, photosynthetic influences on CO2, COS, and CO are correlated in time and space. As such, the covariation of boundary layer enhancements of CO2, COS, and CO can provide clues about the seasonal and spatial distribution of plant carbon uptake. By examining this covariation across diverse regions in the eastern US, we uncovered evidence that humid temperate forests in the previously poorly constrained southern US continue to photosynthesize and absorb CO2 and COS (and emit CO through biogenic volatile organic compound precursor emissions) deeper into the growing season than expected by models and satellite‐constrained estimates. Our results imply that southern US forests have stronger climate sensitivities than currently accounted for in models. Key Points: Atmospheric Carbon Transport‐America airborne flask observations of CO2, carbonyl sulfide, and carbon monoxide (CO) contains information about photosynthetic carbon uptakeBoundary layer enhancements of CO2 and CO show significant anti‐correlation in summer across eastern USComparison to model and data constrained simulations suggest higher than expected photosynthetic carbon uptake in southern evergreen forests [ABSTRACT FROM AUTHOR]

Atmospheric CO2 inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we used ACT‐America airborne observations to compare CO2 model observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(‐Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as CO2 boundary condition for WRF. Both models showed reasonable agreement with observations, and CO2 residuals follow near symmetric peaked (i.e., non‐Gaussian) distribution with near‐zero bias of both models (CT: −0.34±3.12 ppm; WRF: 0.82±4.37 ppm). We also found large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary‐layer biases (1–10 ppm) were much larger than free tropospheric biases (0.5–1 ppm) and were of same magnitude as model‐model differences, whereas free tropospheric biases were mostly governed by CO2 background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for CO2 residuals. While CT could reproduce the principal CO2 dynamics associated with synoptic systems, WRF showed a clearer distinction for CO2 differences across fronts. Variograms were used to quantify spatial correlation of residuals and showed characteristic residual length scales of approximately 100–300 km. Our findings suggest that inclusion of synoptic weather‐dependent and non‐Gaussian error structure may benefit inversion systems. Key Points: CO2 observed by aircraft in Eastern U.S. compares well to models, but residuals are strongly non‐GaussianModel biases affect representation of cross‐frontal CO2 gradients governing CO2 transport in stormsInversion models may benefit from model‐data mismatch errors dependent upon synoptic sector [ABSTRACT FROM AUTHOR]

Terrestrial biosphere models (TBMs) play a key role in the detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon‐climate system. TBM evaluation commonly involves direct comparison to eddy‐covariance flux measurements. We use atmospheric CO2 mole fraction ([CO2]) measured in situ from aircraft and tower, in addition to flux‐measurements from summer 2016 to evaluate the Carnegie‐Ames‐Stanford‐Approach (CASA) TBM. WRF‐Chem is used to simulate [CO2] using biogenic CO2 fluxes from a CASA parameter‐based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO2 boundary condition ensembles. The resulting "super ensemble" of modeled [CO2] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO2] data show that the CASA ensemble net ecosystem exchange (NEE) of CO2 is biased high (NEE too positive) and identify the maximum light use efficiency Emax a key parameter that drives the spread of the CASA ensemble in summer 2016. These findings are verified with flux‐measurements. The direct comparison of the CASA flux ensemble with flux‐measurements confirms missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilate observed [CO2]. Flux tower analyses reveal an unrealistic overestimation of nighttime respiration in CT2017 which we attribute to limited flexibility in the inversion strategy. Key Points: In summer, uncertainty in biogenic fluxes is the largest source of uncertainty in modeled atmospheric CO2Atmospheric CO2 mole fraction and eddy‐flux measurements confirm a sink over North America not represented in the Carnegie‐Ames‐Stanford‐Approach (CASA) terrestrial modelEddy‐flux measurements suggest CASA summer daytime uptake is too weak and nighttime respiration is too strong [ABSTRACT FROM AUTHOR]

Quantification of regional terrestrial carbon dioxide (CO2) fluxes is critical to our understanding of the carbon cycle. We evaluate inverse estimates of net ecosystem exchange (NEE) of CO2 fluxes in temperate North America, and their sensitivity to the observational data used to drive the inversions. Specifically, we consider the state‐of‐the‐science CarbonTracker global inversion system, which assimilates (a) in situ measurements (IS), (b) the Orbiting Carbon Observatory‐2 (OCO‐2) v9 column CO2 (XCO2) retrievals over land (LNLG), (c) OCO‐2 v9 XCO2 retrievals ocean‐glint (OG), and (d) a combination of all these observational constraints (LNLGOGIS). We use independent CO2 observations from the Atmospheric Carbon and Transport (ACT)—America aircraft mission to evaluate the inversions. We diagnose errors in the flux estimates using the differences between modeled and observed biogenic CO2 mole fractions, influence functions from a Lagrangian transport model, Bayesian inference, and root‐mean‐square error (RMSE) and bias metrics. The IS fluxes have the smallest RMSE among the four products, followed by LNLG. Both IS and LNLG outperform the OG and LNLGOGIS inversions with regard to RMSE. Regional errors do not differ markedly across the four sets of posterior fluxes. The CarbonTracker inversions appear to overestimate the seasonal cycle of NEE in the Midwest and Western Canada, and overestimate dormant season NEE across the Central and Eastern US. The CarbonTracker inversions may overestimate annual NEE in the Central and Eastern US. The success of the LNLG inversion with respect to independent observations bodes well for satellite‐based inversions in regions with more limited in situ observing networks. Plain Language Summary: Biological CO2 fluxes, an important component of the earth's climate system, remain uncertain, especially at continental and sub‐continental spatial domains. Different global CO2 observing systems imply significantly different net biological fluxes of CO2. We use independent CO2 measurements from an extensive multi‐seasonal aircraft campaign to evaluate biological CO2 flux estimates derived from four different observational systems entered into a common data analysis system. The observations include both ground and satellite‐based measurements. We found that one of the the satellite‐based CO2 estimates performs nearly as well as the estimates based on ground‐based measurements. This suggests that the satellite data may serve to estimate regional variations in biological CO2 fluxes in portions of the globe with more limited ground‐based observing networks. The inversions all appear to overestimate dormant season release of biological CO2 to the atmosphere, thus may underestimate the net uptake of CO2 by ecosystems in the Central and Eastern United States. Key Points: In situ measurements and the land nadir/land glint inversions are the most reliable products of CarbonTracker in temperate North America, superior to ocean‐glint or LNLGOGIS inversionsErrors in these CarbonTracker regional flux estimates are not strongly dependent on the observational data sourcesCarbonTracker overestimates seasonal net ecosystem exchange (NEE) for the Eastern and Central US, thus the annual NEE may underestimate continental uptake of CO2 [ABSTRACT FROM AUTHOR]

Enhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)‐America mission and their formation mechanism is undetermined. Improved understanding and correct simulation of these CO2 bands are needed for unbiased inverse CO2 flux estimation. Such CO2 bands are hypothesized to be related to nighttime CO2 respiration and investigated in this study using WRF‐VPRM, a weather‐biosphere‐online‐coupled model, in which the biogenic fluxes are handled by the Vegetation Photosynthesis and Respiration Model (VPRM). While the default VPRM satisfactorily parameterizes gross ecosystem exchange, its treatment of terrestrial respiration as a linear function of temperature was inadequate as respiration is a nonlinear function of temperature and also depends on the amount of biomass and soil wetness. An improved ecosystem respiration parameterization including enhanced vegetation index, a water stress factor, and a quadratic temperature dependence is incorporated into WRF‐VPRM and evaluated in a year‐long simulation before applied to the investigation of the frontal CO2 band on August 4, 2016. The evaluation shows that the modified WRF‐VPRM increases ecosystem respiration during the growing season, and improves model skill in reproducing nighttime near‐surface CO2 peaks. A nested‐domain WRF‐VPRM simulation is able to capture the main characteristics of the August 4 CO2 band and informs its formation mechanism. Nighttime terrestrial respiration leads to accumulation of near‐surface CO2 in the region. As the cold front carrying low‐CO2 air moves southeastward, and strong photosynthesis depletes CO2 further southeast of the front, a CO2 band develops immediately ahead of the front. Key Points: A new terrestrial ecosystem respiration parameterization is implemented in WRF‐VPRM to improve the simulation of both respiration and GPPThe daytime bands of elevated CO2 mole fractions ahead of cold fronts form in part due to the accumulation of nighttime respirationCloud shading induced perturbation of photosynthesis cannot explain the CO2 band immediately ahead of the surface cold front on August 4, 2016 [ABSTRACT FROM AUTHOR]

Synoptic weather systems are a major driver of spatial gradients in atmospheric CO2 mole fractions. During frontal passages, air masses from different regions meet at the frontal boundary creating significant gradients in CO2 mole fractions. We quantitatively describe the atmospheric transport of CO2 mole fractions during a mid‐latitude cold front passage and explore the impact of various sources of CO2. We focus here on a cold front passage over Lincoln, Nebraska on August 4th, 2016 observed by aircraft during the Atmospheric Carbon and Transport‐America campaign. A band of air with elevated CO2 was located along the frontal boundary. Observed and simulated differences in CO2 across the front were as high as 25 ppm. Numerical simulations using Weather Research and Forecasting Model with Chemistry at cloud resolving resolutions (3 km), coupled with CO2 surface fluxes and boundary conditions from CarbonTracker (CT‐NRTv2017x), were performed to explore atmospheric transport at the front. Model results demonstrate that the frontal CO2 difference in the upper troposphere can be explained largely by inflow from outside of North America. This difference is modified in the atmospheric boundary layer and lower troposphere by continental surface fluxes, dominated in this case by biogenic and fossil fuel fluxes. Horizontal and vertical advection are found to be responsible for the transport of CO2 mole fractions along the frontal boundary. We show that cold front passages lead to large CO2 transport events including a significant contribution from vertical advection, and that midcontinent frontal boundaries are formed from a complex mixture of CO2 sources. Key Points: High resolution simulation of a cold front passage captures the narrow band of elevated CO2 ahead of the cold frontCO2 inflow from the continental boundaries along with biogenic fluxes create the summertime frontal CO2 distributionHorizontal and vertical advection dominate atmospheric CO2 transport along the frontal boundary [ABSTRACT FROM AUTHOR]

Satellite observations of the total column dry‐air CO2 (XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals to optimize whole‐city emissions, using a Bayesian inversion system and high‐resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: "plume city" located in relatively flat terrain, represented by Riyadh and Cairo; and "basin city" located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least numbers of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 8, 5, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffXCO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local XCO2 enhancements associated with local biospheric XCO2 variations are shown, which potentially lead to biased estimates of ffCO2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components. Key Points: Inversion method is utilized to constrain whole‐city fossil fuel emissions with measurement and transport model errors consideredPotential of incorporating multiple tracks to obtain regular emission estimates is evaluated by pseudo data experimentsSignificant contribution of the biospheric fluxes variability to local XCO2 variation is demonstrated [ABSTRACT FROM AUTHOR]

Our knowledge about synoptic‐scale variations of atmospheric‐CO2 produced by interactions between midlatitude cyclones and regional‐scale surface fluxes remains limited due to the scarcity of observations. We synthesized observations of greenhouse gases (GHGs) with respect to frontal passages to understand how GHG distributions change vertically and horizontally during a synoptic event. We use the airborne in situ measurements of GHGs collected during the Atmospheric Carbon and Transport‐America summer 2016 field campaign. Using these measurements, we defined three metrics, (1) the differences in the GHG mole fractions across frontal boundaries in the atmospheric boundary layer (BL) and free troposphere (FT), (2) differences in the vertical contrasts in GHGs in warm and cold sectors, and (3) the size and magnitude of enhanced CO2 in the vicinity of frontal boundary. We found that frontal structures are clearly associated with spatially coherent and significant changes in GHG composition. Warm sector CO2 mole fractions [CO2] are higher than those in the cold sector. The cross‐frontal [CO2] contrasts are largest in the BL (5–30 ppm) with smaller differences in the FT (3–5 ppm). We found higher [CH4] in the BL in the warm sector than in the cold sector for 5 out of 11 cases. Analyses of vertical profiles revealed higher [CO2] in the FT than in the BL in the cold sector while opposite pattern in the warm sector. Average BL‐to‐FT [CO2] differences are 12 and −6 ppm in the warm and cold sectors, respectively. The observational analyses presented define new metrics involving horizontal and vertical GHG contrasts across fronts during summer which will be used to evaluate simulations of GHG transport. Key Points: We report the first comprehensive airborne observations of the horizontal and vertical variability in atmospheric greenhouse gases (CO2, CH4) associated with midlatitude cyclonesCold fronts contain coherent CO2 structures including cross‐frontal contrasts at all levels in the troposphere and a narrow band of enhanced CO2 along the frontal boundaryFronts showed lower [CO2] in the cold sector (north/west) and higher [CO2] in the warm sector (south/east), with larger contrasts in boundary layer than in free troposphere [ABSTRACT FROM AUTHOR]

Accurate and fine‐scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model‐intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed‐parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3‐hourly scales for North America at ~500‐m and 5‐km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (Emax), optimal temperature of photosynthesis (Topt), and temperature response of respiration (Q10). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned Emax with site‐level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site‐level results at both monthly and 3‐hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. The ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America. Key Points: By perturbing three key model parameters, the mean of the new ensemble outperforms other model products in comparison to flux tower dataSeasonal net carbon exchange shows consistency with other model products in high‐ and middle‐latitude regions and at continental scalesCold‐season respiration needs to be suppressed and maximum light use efficiency needs to be varied seasonally [ABSTRACT FROM AUTHOR]

Regional estimates of biogenic carbon fluxes over North America from both atmospheric inversions ("top‐down" approach) and terrestrial biosphere models ("bottom‐up") remain highly uncertain. We merge these approaches with an ensemble‐based, regional modeling system able to diagnose and quantify the causes of uncertainties in top‐down atmospheric estimates of the terrestrial sink over North America. Our ensemble approach quantifies and partitions the uncertainty stemming from atmospheric transport, the biosphere, and large‐scale CO2 boundary inflow (boundary conditions). We use meteorological data, CO2 fluxes, and CO2 mole fraction measurements to assure the reliability of the ensemble system. Our results show that all uncertainty components have clear seasonal variations. The biogenic flux component dominates modeled boundary layer CO2 uncertainty, ranging from 2.5 ppm in summer and winter to 1.5 ppm in fall and spring. Spatially, it remains highly uncertain in the U.S. Corn Belt regions. Transport uncertainty reaches a maximum of 2.5 ppm in the summer months and stays at 1.2 ppm for the rest of the year and is highly correlated with the biogenic CO2 fluxes. Boundary conditions play the smallest role in atmospheric boundary layer CO2 uncertainty with a magnitude smaller than 1 ppm. However, boundary conditions are the most important uncertainty component in column‐averaged CO2 (XCO2). The spatiotemporal variations of the uncertainties in modeled XCO2 are similar to those in atmospheric boundary layer CO2. Plain Language Summary: Uncertainty in future uptake of carbon dioxide (CO2) by terrestrial ecosystems drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. North American ecosystems are currently a significant sink of atmospheric CO2, but this sink is difficult to measure and thus understand. Terrestrial uptake and emission of CO2 from a continent such as North America can be inferred from atmospheric CO2 observations. This approach, however, requires an accurate understanding of all the sources of uncertainty in simulating atmospheric CO2. These include errors in the simulations of atmospheric transport, fossil fuel emissions of CO2, and transport of CO2 from the rest of the Earth's continents and oceans (boundary conditions), in addition to the uncertainty terrestrial CO2 uptake. Here we quantify the uncertainty in all of these components of an atmospheric CO2 simulation over North America using an ensemble of simulations. Observations of atmospheric winds and mixing, atmospheric CO2 mole fractions, and land‐atmosphere CO2 fluxes are used to ensure that the ensemble cover an appropriate range of uncertainty in each element of our simulation. Our results show that over North America, (i) terrestrial biosphere CO2 fluxes dominate the uncertainty in the atmospheric boundary layer CO2, (ii) atmospheric transport uncertainty is highly correlated with biosphere flux uncertainty in space and time, and (iii) inflow of CO2 from other continents and oceans plays the least important role in North American lower atmosphere CO2 but is the largest source of uncertainty in atmospheric column‐integrated CO2. Our study shows that atmospheric transport and continental boundary conditions are important limits to our ability to infer terrestrial ecosystem CO2 uptake using, respectively lower atmosphere and atmospheric column CO2 measurements. Key Points: Our calibrated ensemble system demonstrates that biogenic fluxes dominate the uncertainty in atmospheric boundary layer CO2Transport uncertainty is highly correlated with biogenic flux uncertainty in space and timeLarge‐scale CO2 boundary inflow dominates uncertainty in the atmospheric column‐integrated CO2 uncertainty [ABSTRACT FROM AUTHOR]

Atmospheric inversions allow us to estimate the terrestrial carbon sink by combining atmospheric observations with atmospheric transport models. However, these inverse estimates remain highly uncertain. Here we quantify uncertainties in simulations of North American atmospheric CO2 concentrations using a probabilistic approach. We demonstrate that uncertainty in fossil fuel emissions is a key factor in the uncertainty surrounding biospheric flux estimates. We show that atmospheric transport uncertainties in state‐of‐the‐art numerical weather models diminish when averaged over time, while uncertainties in large‐scale CO2 boundary inflow considerably impair our ability to quantify regional fluxes. Current estimates of the North America land sink that neglect the uncertainties in CO2 boundary inflow and fossil fuel emissions are likely overconfident. Our findings suggest that targeted use of new atmospheric observations and improved quantification of uncertainty components are a promising avenue to improve atmospheric inversions with the goal to refine estimates of biospheric CO2 fluxes on regional and continental scales. Plain Language Summary: The uncertainty in biospheric carbon dioxide (CO2) flux estimates drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. The terrestrial carbon sink can be inferred from atmospheric CO2 observations with transport models via inversion methods. Regional CO2 flux estimates remain uncertain due to the mixture of uncertainties caused by transport models, prior estimates of biospheric fluxes, large‐scale CO2 boundary inflow, the assumptions in the inversion process, and the limited density of atmospheric CO2 observations. Understanding the characteristics of these uncertainties in space and time is essential for accurate biospheric CO2 flux estimates. Here we identify the terms that most confound biospheric flux estimates. Our results show that, over North America, (i) biospheric fluxes dominate the model uncertainty over all timescales. (ii) Contrary to expectation, fossil fuel emissions are the second largest source of uncertainty at all timescales. (iii) Transport uncertainties are large at short timescales but act like random errors decreasing with time averaging. (ix) Continental boundary inflow uncertainties are large near the boundaries and become significant at seasonal to annual timescales. We propose sampling and analysis strategies that can better quantify and reduce uncertainties in both fossil fuel emission and biospheric flux estimates. Key Points: We quantify biospheric flux, fossil fuel emission, atmospheric transport, and boundary inflow uncertainties in modeled atmospheric CO2Biospheric fluxes and fossil fuel emissions are the largest contributors to atmospheric CO2 uncertainty over North AmericaTransport uncertainties can be approximated by random errors, while boundary inflow uncertainties are persistent and can be sizeable [ABSTRACT FROM AUTHOR]

Inference of CO2 surface fluxes using atmospheric CO2 observations in atmospheric inversions depends critically on accurate representation of atmospheric transport. Here we characterize regional‐scale CO2 transport uncertainties due to uncertainties in meteorological fields using a mesoscale atmospheric model and an ensemble of simulations with flow‐dependent transport errors. During a 1‐month summer period over North America, transport uncertainties yield an ensemble spread in instantaneous CO2 at 100 m above ground level comparable to the CO2 uncertainties resulting from 48% relative uncertainty in 3‐hourly natural CO2 fluxes. Temporal averaging reduces transport uncertainties but increases the influence of CO2 uncertainties from the lateral boundaries. The influence of CO2 background uncertainties is especially large for column‐averaged CO2. These results suggest that transport errors and CO2 background errors limit regional atmospheric inversions at two distinct timescales and that the error characteristics of transport and background errors should guide the design of regional inversion systems. Plain Language Summary: Accurate estimates of regional‐scale CO2 surface fluxes are essential to improve our understanding of the carbon cycle and to verify human CO2 emission inventories. CO2 surface fluxes can be inferred from atmospheric CO2 measurements through inversion methods, which use atmospheric transport models to relate CO2 concentration to fluxes. However, previous studies have shown that inversion results can be sensitive to errors in the simulated atmospheric transport. To better understand how to account for such transport errors, we characterize the uncertainties in simulated CO2 concentration due to uncertainties in atmospheric transport by running an ensemble of perturbed transport simulations in a regional atmospheric model. Our results show that CO2 uncertainties due to transport uncertainties are about half the magnitude as uncertainties due to erroneous CO2 surface fluxes while displaying similar spatial and temporal patterns. Transport uncertainties are reduced when CO2 is time averaged, but at the same time the influence of uncertainties in the CO2 background concentration is increased at longer timescales. Thus, the flux signals in regional inversions are degraded by transport errors and CO2 background errors at different timescales, and it is imperative to properly account for these errors to obtain reliable regional‐scale CO2 flux estimates. Key Points: We quantify CO2 transport uncertainties due to uncertainties in meteorological fields at the regional scale and submonthly timescalesAtmospheric CO2 estimates that are strongly influenced by CO2 surface fluxes tend to also have large transport uncertaintiesThe signal‐to‐noise ratio in in situ CO2 is limited by transport errors at subweekly timescales and CO2 background errors at longer scales [ABSTRACT FROM AUTHOR]

This paper studies laser cleaning for mini & micro COG products. Research shows that laser cleaning has great advantages, which can realize accurate position cleaning and remove various pollution. The adjustment of laser cleaning process parameters can improve the adhesion of substrate surface and realize mass production application on the premise of ensuring low surface damage. [ABSTRACT FROM AUTHOR]

With the remarkable growth in rich media in recent years, people are increasingly exposed to visual information from the environment. Visual information continues to play a vital role in rich media because people's real interests lie in dynamic information. This paper proposes a novel discrete dynamic swarm optimization (DDSO) algorithm for video object tracking using invariant features. The proposed approach is designed to track objects more robustly than other traditional algorithms in terms of illumination changes, background noise, and occlusions. DDSO is integrated with a matching procedure to eliminate inappropriate feature points geographically. The proposed novel fitness function can aid in excluding the influence of some noisy mismatched feature points. The test results showed that our approach can overcome changes in illumination, background noise, and occlusions more effectively than other traditional methods, including color-tracking and invariant feature-tracking methods. [ABSTRACT FROM AUTHOR]

The enantioselective allylic amination of Morita-Baylis-Hillman acetates catalyzed by chiral cyclohexane-based thiourea-phosphine catalysts was investigated. In the presence of 20 mol% rosin-derived thiourea-phosphine 3j, the chiral amines were obtained in up to 88% yield and up to 85% ee. [ABSTRACT FROM AUTHOR]

Effective Schwann cells (SCs) attachment is a prerequisite for the successful construction of tissue-engineered nerve. The present study aimed to investigate the role of an avidin-biotin binding system (ABBS) for neural tissue engineering. The attachment, proliferation, and morphology of biotinylated SCs on avidin-treated scaffolds were examined, and the effects of avidin, biotin, and the avidin-biotin binding system on SCs gene expressions were also studied. The results indicated that the attachment of biotinylated SCs onto avidin-treated scaffolds was promoted obviously within a short time (10 min). Meanwhile, there were no great differences in terms of proliferation and morphology of SCs between the two groups after cultivation for 14 days. The gene expressions of S100, GDNF, BDNF, NGF, CNTF, and PMP22 were up-regulated significantly by biotin rather than aligned scaffolds or avidin. The present study demonstrated that ABBS enhanced the attachment and maturation of SCs onto the electrospun scaffolds without adverse effects on the proliferation of SCs in the long term, suggesting the potential application of ABBS in the neural tissue engineering. © 2011 Wiley Periodicals, Inc. J Biomed Mater Res Part A, 2011. [ABSTRACT FROM AUTHOR]

Diagnosing carbon dioxide (CO2) and methane (CH4) fluxes at subcontinental scales is complicated by sparse observations, limited knowledge of prior fluxes and their uncertainties, and background and transport errors. Multispecies measurements in flasks sampled during the wintertime ACT‐America campaign were used for background characterization and source apportionment of regional anthropogenic CO2 and CH4 fluxes when ecosystem CO2 exchange is likely to be least active. Continental background trace gas mole fractions for regional enhancements are defined using samples from the upper troposphere and assessed using model products. Trace gas enhancements taken from flask samples in the lower troposphere with background levels subtracted out are then interpreted to inform CO2 and CH4 enhancement variability in the eastern United States. Strong correlations between CO2 and CH4 enhancements in the Midwestern and Mid‐Atlantic United States indicated colocated anthropogenic sources. Oil and natural gas influence was also broadly observed throughout the entire observational domain. In the Midwestern United States, agricultural influence on CO2 and CH4 enhancement variability was evident during above‐average wintertime temperatures. Weaker correlations between CO2 and anthropogenic tracer enhancements in the Southeastern United States indicated potentially nonnegligible wintertime ecosystem CO2 exchange, with biogenic tracers indicating more active surface processing than other regions. These whole‐air flask samples illuminated significant regional CO2 and CH4 sources or sinks during Atmospheric Carbon and Transport‐America (ACT‐America) and can provide additional information for informing regional inverse modeling efforts. Key Points: Multispecies flask samples are used to characterize regional wintertime eastern U.S. CO2 and CH4 enhancement variabilityMid‐Atlantic and Midwestern U.S. CO2 and CH4 enhancements are primarily anthropogenicWinter Southeastern U.S. CO2 and CH4 enhancements indicate influence from biogenic exchange and surface processing [ABSTRACT FROM AUTHOR]