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Accurate hydrologic modeling is vital to characterizing how the terrestrial water cycle responds to climate change. Pure deep learning (DL) models have been shown to outperform process-based ones while remaining difficult to interpret. More recently, differentiable physics-informed machine learning models with a physical backbone can systematically integrate physical equations and DL, predicting untrained variables and processes with high performance. However, it is unclear if such models are competitive for global-scale applications with a simple backbone. Therefore, we use – for the first time at this scale – differentiable hydrologic models (full name δHBV-globe1.0-hydroDL, shortened to δHBV here) to simulate the rainfall–runoff processes for 3753 basins around the world. Moreover, we compare the δHBV models to a purely data-driven long short-term memory (LSTM) model to examine their strengths and limitations. Both LSTM and the δHBV models provide competitive daily hydrologic simulation capabilities in global basins, with median Kling–Gupta efficiency values close to or higher than 0.7 (and 0.78 with LSTM for a subset of 1675 basins with long-term discharge records), significantly outperforming traditional models. Moreover, regionalized differentiable models demonstrated stronger spatial generalization ability (median KGE 0.64) than a traditional parameter regionalization approach (median KGE 0.46) and even LSTM for ungauged region tests across continents. Nevertheless, relative to LSTM, the differentiable model was hampered by structural deficiencies for cold or polar regions, highly arid regions, and basins with significant human impacts. This study also sets the benchmark for hydrologic estimates around the world and builds a foundation for improving global hydrologic simulations.

Subalpine forests are a crucial component of the carbon cycling system in the Qinghai–Tibet Plateau (QTP). However, there are currently significant data gaps in the QTP, and it is essential to enhance continuous monitoring of forest carbon absorption processes in the future. This study investigates 2 years' carbon exchange dynamics of a subalpine forest on the QTP using an eddy covariance method. We first characterized the seasonal carbon dynamics of the subalpine forest, revealing the higher carbon dioxide (CO2) exchange rates in summer and autumn and lower rates in winter and spring, and found that autumn is the peak period for carbon sequestration in this subalpine forest, with the maximum measured value of CO2 absorption reaching 10.70 µmol m−2 s−1. Subsequently, we examined the environmental factors influencing the carbon sequestration function. Principal component analysis (PCA) shows that photosynthetically active radiation (PAR) was the major environmental factor driving the net ecosystem exchange (NEE) of CO2, significantly influencing forest carbon absorption, and the increase in relative humidity decreases the rate of carbon fixation. In addition, we explored NEE and its influencing factors at the regional scale and found that air temperature promotes carbon dioxide absorption (negative NEE values), while the average annual precipitation shows a minor effect on NEE. At the annual scale, the subalpine forest functions as a strong carbon sink, with an average NEE of −332 to −351 g C m−2 (from November 2020 to October 2022). Despite the challenges of climate change, forests remain robust carbon sinks with the highest carbon sequestration capacity in the QTP, with an average annual CO2 absorption rate of 368 g C m−2. This study provides valuable insights into the carbon cycling mechanism in subalpine ecosystems and the global carbon balance.

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

Predicting the response of hydrologic systems to modified driving forces beyond patterns that have occurred in the past is of high importance for estimating climate change impacts or the effect of management measures. This kind of prediction requires a model, but the impossibility of testing such predictions against observed data makes it difficult to estimate their reliability. Metamorphic testing offers a methodology for assessing models beyond validation with real data. It consists of defining input changes for which the expected responses are assumed to be known, at least qualitatively, and testing model behavior for consistency with these expectations. To increase the gain of information and reduce the subjectivity of this approach, we extend this methodology to a multi-model approach and include a sensitivity analysis of the predictions to training or calibration options. This allows us to quantitatively analyze differences in predictions between different model structures and calibration options in addition to the qualitative test of the expectations. In our case study, we apply this approach to selected conceptual and machine learning hydrological models calibrated for basins from the CAMELS data set. Our results confirm the superiority of the machine learning models over the conceptual hydrologic models regarding the quality of fit during calibration and validation periods. However, we also find that the response of machine learning models to modified inputs can deviate from the expectations and the magnitude, and even the sign of the response can depend on the training data. In addition, even in cases in which all models passed the metamorphic test, there are cases in which the quantitative response is different for different model structures. This demonstrates the importance of this kind of testing beyond and in addition to the usual calibration–validation analysis to identify potential problems and stimulate the development of improved models.

Isoprene has the largest global non-methane hydrocarbon emission, and the oxidation of isoprene plays a crucial role in the formation of secondary organic aerosol (SOA). Two primary processes are known to contribute to SOA formation from isoprene oxidation: (1) the reactive uptake of isoprene-derived epoxides on acidic or aqueous particle surfaces and (2) the absorptive gas–particle partitioning of low-volatility oxidation products. In this study, we developed a new multiphase condensed isoprene oxidation mechanism that includes these processes with key molecular intermediates and products. The new mechanism was applied to simulate isoprene gas-phase oxidation products and SOA formation from previously published chamber experiments under a variety of conditions and atmospheric observations during the Southern Oxidant and Aerosol Studies (SOAS) field campaign. Our results show that SOA formation from most of the chamber experiments is reasonably reproduced using our mechanism, except when the concentration ratios of initial nitric oxide to isoprene exceed ∼ 2, the formed SOA is significantly underpredicted. The SOAS simulations also reasonably agree with the measurements regarding the diurnal pattern and concentrations of different product categories, while the total isoprene SOA remains underestimated. The molecular compositions of the modeled SOA indicate that multifunctional low-volatility products contribute to isoprene SOA more significantly than previously thought, with a median mass contribution of ∼ 57 % to the total modeled isoprene SOA. However, this contribution is intricately intertwined with IEPOX-derived SOA (IEPOX: isoprene-derived epoxydiols), posing challenges for their differentiation using bulk aerosol composition analysis (e.g., the aerosol mass spectrometer with positive matrix factorization). Furthermore, the SOA from these pathways may vary greatly, mainly dependent on the volatility estimation and treatment of particle-phase processes (i.e., photolysis and hydrolysis). Our findings emphasize that the various pathways to produce these low-volatility species should be considered in models to more accurately predict isoprene SOA formation. The new condensed isoprene chemical mechanism can be further incorporated into regional-scale air quality models, such as the Community Multiscale Air Quality Modelling System (CMAQ), to assess isoprene SOA formation on a larger scale.

In situ surface wind observation is a critical meteorological data source for various research fields. However, data quality is affected by factors such as surface friction changes, station relocations and anemometer updates. Previous methods to address discontinuities have been insufficient, and processing methods have not always adhered to World Meteorological Organization (WMO) World Climate Programme guidelines. We analyzed data discontinuity caused by anemometer changes and station relocations in China's daily in situ near-surface (∼ 10 m) wind speed observations and the impact of the processing methods on wind speed trends. By comparing the wind speed discontinuities with the recorded location changes, we identified 90 stations that showed abnormally increasing wind speeds due to relocation. After removing those stations, we followed a standard quality control method recommended by the World Meteorological Organization to improve the data reliability and applied Thiessen polygons to calculate the area-weighted average wind speed. The result shows that China's recent reversal of wind speed was reduced by 41 % after removing the problematic stations, with an increasing trend of 0.017 m s−1 yr−1 (R2 = 0.64, P < 0.05), emphasizing the importance of robust quality control and homogenization protocols in wind trend assessments.

This study focuses on the development of a multi-point technique for future constellation missions, aiming to measure gradients at various orders, in particular the linear and quadratic gradients, of a general field. It is well established that, in order to estimate linear gradients, the spacecraft must not lie on a plane. Through analytical exploration within the framework of least squares, it is demonstrated that at least 10 spacecraft that do not lie on any quadric surface are required to estimate both linear and quadratic gradients. The spatial arrangement of the spacecraft can be characterized by a set of quality factors. In cases where there is poor temporal synchronization among the spacecraft leading to non-simultaneous measurements, temporal gradients must be included. If the spacecraft have multiple velocities, by incorporating temporal gradients it is possible to reduce the number of required spacecraft. Furthermore, it is proved that the accuracy of the linear gradient is of second order and that of the quadratic gradient is of first order. Additionally, a method for estimating errors in the calculation is also illustrated.

Background: Nearly one-third of patients with newly diagnosed pancreatic cancer present with locally advanced disease (LAPC), with 25% eligible for surgical resection, lending to a poor 5-year overall survival of 16.2%. Given the significant morbidity and mortality associated with LAPC, timely integration of palliative and supportive care (SC) into the treatment care plan is vital. The purpose of this study was to investigate the utilization of SC in patients with LAPC and identify the demographic and socioeconomic factors that influence its application. Patients and methods: A retrospective database analysis of the National Cancer Database (NCDB) was carried out. Data regarding patients diagnosed with stage II-III LAPC between 2004 and 2018 were used. Analyses included tumor characteristics, demographics, socioeconomic parameters, and trends in utilization of SC. Results: A total of 111 964 patients were included [stage II (72.3%); stage III (27.6%)]. Only 7.72% received SC despite 67% of patients receiving cancer-directed treatment at an academic or integrated network cancer program, 84% living in or near a metro area, and 60% living ≤20 miles of their primary treatment center. Rates of SC utilization remained

The Indonesia Throughflow (ITF) is the only low-latitude channel between the Pacific and Indian oceans, and its variability has important effects on global climate and biogeochemical cycles. Climate models consistently predict a decline in ITF transport under global warming, but it has not yet been examined under solar geoengineering scenarios. We use standard parameterized methods for estimating the ITF – the Amended Island Rule and buoyancy forcing – to investigate the ITF under the SSP2-4.5 and SSP5-8.5 greenhouse gas scenarios and the geoengineering experiments G6solar and G6sulfur, which reduce net global mean radiative forcing from SSP5-8.5 levels to SSP2-4.5 levels using solar dimming and sulfate aerosol injection strategies, respectively. Six-model ensemble-mean projections for 2080–2100 show reductions of 19 % under the G6solar scenario and 28 % under the G6sulfur scenario relative to the historical (1980–2014) ITF, which should be compared with reductions of 23 % and 27 % under SSP2-4.5 and SSP5-8.5. Despite standard deviations amounting to 5 %–8 % for each scenario, all scenarios are significantly different from each other (p) when the whole 2020–2100 simulation period is considered. Thus, significant weakening of the ITF occurs under all scenarios, but G6solar more closely approximates SSP2-4.5 than G6sulfur does. In contrast with the other three scenarios, which show only reductions in forcing due to ocean upwelling, the G6sulfur experiment shows a large reduction in ocean surface wind stress forcing accounting for 47 % (38 %–65 % across the model range) of the decline in wind + upwelling-driven ITF transport. There are also reductions in deep-sea upwelling in extratropical western boundary currents.

Photosynthesis plays an important role in carbon, nitrogen, and water cycles. Ecosystem models for photosynthesis are characterized by many parameters that are obtained from limited in situ measurements and applied to the same plant types. Previous site-by-site calibration approaches could not leverage big data and faced issues like overfitting or parameter non-uniqueness. Here we developed an end-to-end programmatically differentiable (meaning gradients of outputs to variables used in the model can be obtained efficiently and accurately) version of the photosynthesis process representation within the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) model. As a genre of physics-informed machine learning (ML), differentiable models couple physics-based formulations to neural networks (NNs) that learn parameterizations (and potentially processes) from observations, here photosynthesis rates. We first demonstrated that the framework was able to correctly recover multiple assumed parameter values concurrently using synthetic training data. Then, using a real-world dataset consisting of many different plant functional types (PFTs), we learned parameters that performed substantially better and greatly reduced biases compared to literature values. Further, the framework allowed us to gain insights at a large scale. Our results showed that the carboxylation rate at 25 ∘C (Vc,max25) was more impactful than a factor representing water limitation, although tuning both was helpful in addressing biases with the default values. This framework could potentially enable substantial improvement in our capability to learn parameters and reduce biases for ecosystem modeling at large scales.

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Climate change threatens our ability to grow food for an ever-increasing population. There is a need for high-quality soil moisture predictions in under-monitored regions like Africa. However, it is unclear if soil moisture processes are globally similar enough to allow our models trained on available in situ data to maintain accuracy in unmonitored regions. We present a multitask long short-term memory (LSTM) model that learns simultaneously from global satellite-based data and in situ soil moisture data. This model is evaluated in both random spatial holdout mode and continental holdout mode (trained on some continents, tested on a different one). The model compared favorably to current land surface models, satellite products, and a candidate machine learning model, reaching a global median correlation of 0.792 for the random spatial holdout test. It behaved surprisingly well in Africa and Australia, showing high correlation even when we excluded their sites from the training set, but it performed relatively poorly in Alaska where rapid changes are occurring. In all but one continent (Asia), the multitask model in the worst-case scenario test performed better than the soil moisture active passive (SMAP) 9 km product. Factorial analysis has shown that the LSTM model's accuracy varies with terrain aspect, resulting in lower performance for dry and south-facing slopes or wet and north-facing slopes. This knowledge helps us apply the model while understanding its limitations. This model is being integrated into an operational agricultural assistance application which currently provides information to 13 million African farmers.

A new inlet for studying the aerosol particles and hydrometeor residuals that compose mixed-phase clouds – the phaSe seParation Inlet for Droplets icE residuals and inteRstitial aerosol particles (SPIDER) – is described here. SPIDER combines a large pumped counterflow virtual impactor (L-PCVI), a flow tube evaporation chamber, and a pumped counterflow virtual impactor (PCVI) to separate droplets, ice crystals (∼3–25 µm), and interstitial aerosol particles for simultaneous sampling. Laboratory verification tests of each individual component and the composite SPIDER system were conducted. Transmission efficiency, evaporation, and ice crystals' survival were determined to show the capability of the system. The experiments show the SPIDER system can separate distinct cloud elements and interstitial aerosol particles for subsequent analysis. As a field instrument, SPIDER will help explore the properties of different cloud elements and interstitial aerosol particles in mixed-phase clouds.

Seawater intrusion in island aquifers was considered analytically, specifically for annulus segment aquifers (ASAs), i.e., aquifers that (in plan) have the shape of an annulus segment. Based on the Ghijben–Herzberg and hillslope-storage Boussinesq equations, analytical solutions were derived for steady-state seawater intrusion in ASAs, with a focus on the freshwater–seawater interface and its corresponding watertable elevation. Predictions of the analytical solutions compared well with experimental data, and so they were employed to investigate the effects of aquifer geometry on seawater intrusion in island aquifers. Three different ASA geometries were compared: convergent (smaller side is facing the lagoon, larger side is the internal no-flow boundary and flow converges towards the lagoon), rectangular and divergent (smaller side is the internal no-flow boundary, larger side is facing the sea and flow diverges towards the sea). Depending on the aquifer geometry, seawater intrusion was found to vary greatly, such that the assumption of a rectangular aquifer to model an ASA can lead to poor estimates of seawater intrusion. Other factors being equal, compared with rectangular aquifers, seawater intrusion is more extensive, and watertable elevation is lower in divergent aquifers, with the opposite tendency in convergent aquifers. Sensitivity analysis further indicated that the effects of aquifer geometry on seawater intrusion and watertable elevation vary with aquifer width and distance from the circle center to the inner arc (the lagoon boundary for convergent aquifers or the internal no-flow boundary for divergent aquifers). A larger aquifer width and distance from the circle center to the inner arc weaken the effects of aquifer geometry, and hence differences in predictions for the three geometries become less pronounced.

Over the hours of 05:00–09:00 UT on 8 June 2001, the extreme ultraviolet (EUV) instrument on board the IMAGE satellite observed a shoulder-like formation in the morning sector and a post-noon plume-like structure. The plasmapause formation is simulated using the test particle model (TPM), based on a drift motion theory, which reproduces various plasmapause structures and evolution of the shoulder feature. The analysis indicates that the shoulder is created by sharp reduction and spatial non-uniformity in the dawn–dusk convection electric field intensity. The TPM-modeled event is found to develop an initial pre-dawn asymmetric bulge that becomes a shoulder as a result of increased “corotation” rate with an increasing L-shell that is preceded by localized outward convection. The shoulder structure rotates sunward and develops into a single- or double-plume structure during an active time period in simulation.

Submicron particles larger than 300 nm dominate the aerosol light extinction and mass concentration in the atmosphere. The water uptake ability of this size range greatly influences the particle mass, visibility degradation, and particle chemistry. However, most previous field measurements on aerosol hygroscopicity are limited within 350 nm. In this study, the size-resolved aerosol hygroscopic properties over an extended size range (50–600 nm) at 85 % relative humidity were investigated in Beijing winter from 27 November 2019 to 14 January 2020 using a humidity tandem differential mobility analyzer (HTDMA) instrument. The corresponding aerosol optical properties were also analyzed using the Mie scattering theory. Results show that the averaged probability distribution of growth factor (GF-PDF) is generally constituted by a more hygroscopic (MH) group and a less hygroscopic (LH) group (including hydrophobic). For the particles larger than 300 nm, there exists a large fraction of LH group particles, resulting in an unexpected low hygroscopicity. During the development of pollution, when particles are gradually aged and accumulated, the bulk hygroscopicity above 300 nm is enhanced significantly by the growth and expansion of the MH group. This result is supported by previous chemical composition analysis, and we give more direct and detailed evidence from the growth factor and mixing-state aspects. Our calculations indicate that the optical contribution of particles larger than 300 nm constitutes about two-thirds of the total aerosol extinction. The large hygroscopic variation in aerosols above 300 nm will influence the light degradation comparably with the increase in aerosol loading in low-visibility haze events. Our studies highlight that the hygroscopic properties above 300 nm are complex and vary greatly with different pollution stages. Therefore more field measurements and investigations need to be done in the future.

The humidified tandem differential mobility analyzer (HTDMA) is widely used to measure the hygroscopic properties of submicron particles. The size-resolved aerosol hygroscopicity κ measured by a HTDMA will be influenced by the contribution of multiply charged aerosols, but this effect on field measurements has seldom been discussed for previous field measurements. Our calculations demonstrate that the number ratio of multiply charged particles is quite considerable for some specific sizes between 100 and 300 nm, especially during a pollution episode. The presence of multiple charges will lead to a compression effect on the aerosol hygroscopicity in HTDMA measurements. Therefore, we propose a new algorithm that performs multi-charge correction of the size-resolved hygroscopicity κ, taking both the compression effect and the multi-charge number contribution into consideration. Application of the algorithm to field measurements showed that the relatively high hygroscopicity in the accumulation size range leads to the overestimation of the hygroscopicity of particles smaller than 200 nm. The low hygroscopicity of coarse-mode particles leads to the underestimation of the hygroscopicity of accumulation particles between 200 and 500 nm in size. The difference between the corrected and measured κ values can be as large as 0.05, highlighting that special attention must be paid to the multi-charge effect when a HTDMA is used for aerosol hygroscopicity measurements.

Atmospheric black carbon (BC) is the strongest solar radiative absorber in the atmosphere, exerting significant influences on the earth's radiation budget. The mass absorption cross section (MAC) is a crucial parameter for converting the light absorption coefficient (σab) to the equivalent BC mass concentration (EBC). Traditional filter-based instruments, such as the AE33, use a constant MAC of 7.77 m2/g at 880 nm to derive the EBC, which may lead to uncertainty in the EBC. In this paper, a new method of converting σab to the EBC is proposed by incorporating the variations of the MAC attributed to the influences of the aerosol coating state. A Mie simulation showed that the MAC varied dramatically with different core sizes and shell thicknesses. We compared our new method with the traditional method during a field measurement at a site on the North China Plain. The results showed that the MAC at 880 nm was smaller (larger) than 7.77 m2/g for particles smaller (larger) than 280 nm, resulting in an EBC mass size distribution derived from the new method that was higher (lower) than the traditional method for particles smaller (larger) than 280 nm. The size-integrated EBC derived from the new method was 16 % higher than that derived from the traditional method. Sensitivity analysis indicated that the uncertainty in the EBC caused by the refractive index (RI) was within 35 %, and the imaginary part of the RI had dominant influence on the derived EBC. This study emphasizes the necessity to take variations of the MAC into account when deriving the EBC from σab and can help constrain the uncertainty in EBC measurements.

Emissions of ice-nucleating particles (INPs) from sea spray can impact climate and precipitation by changing cloud formation, precipitation, and albedo. However, the relationship between seawater biogeochemistry and the ice nucleation activity of sea spray aerosols remains unclarified. Here, we demonstrate a link between the biological productivity in seawater and the ice nucleation activity of sea spray aerosol under conditions relevant to cirrus and mixed-phase cloud formation. We show for the first time that aerosol particles generated from both subsurface and microlayer seawater from the highly productive eastern tropical North Pacific Ocean are effective INPs in the deposition and immersion freezing modes. Seawater particles of composition similar to subsurface waters of highly productive regions may therefore be an unrealized source of effective INPs. In contrast, the subsurface water from the less productive Florida Straits produced less effective immersion mode INPs and ineffective depositional mode INPs. These results indicate that the regional biogeochemistry of seawater can strongly affect the ice nucleation activity of sea spray aerosol.

There is evidence that black carbon (BC) particles may affect cirrus formation and, hence, global climate by acting as potential ice nucleating particles (INPs) in the troposphere. Nevertheless, the ice nucleation (IN) ability of bare BC and BC coated with secondary organic aerosol (SOA) material remains uncertain. We have systematically examined the IN ability of 100–400 nm size-selected BC particles with different morphologies and different SOA coatings representative of anthropogenic (toluene and n-dodecane) and biogenic (β-caryophyllene) sources in the cirrus regime (−46 to −38 ∘C). Several BC proxies were selected to represent different particle morphologies and oxidation levels. Atmospheric aging was further replicated with the exposure of SOA-coated BC to OH. The results demonstrate that the 400 nm hydrophobic BC types nucleate ice only at or near the homogeneous freezing threshold. Ice formation at cirrus temperatures below homogeneous freezing thresholds, as opposed to purely homogeneous freezing, was observed to occur for some BC types between 100 and 200 nm within the investigated temperature range. More fractal BC particles did not consistently act as superior INPs over more spherical ones. SOA coating generated by oxidizing β-caryophyllene with O3 did not seem to affect BC IN ability, probably due to an SOA-phase state transition. However, SOA coatings generated from OH oxidation of various organic species did exhibit higher IN-onset supersaturation ratio with respect to ice (SSi), compared with bare BC particles, with the toluene-SOA coating showing an increase in SSi of 0.1–0.15 while still below the homogeneous freezing threshold. Slightly oxidized toluene SOA coating seemed to have a stronger deactivation effect on BC IN ability than highly oxidized toluene SOA, which might be caused by oligomer formation and the phase state transition of toluene SOA under different oxidation levels. n-dodecane and β-caryophyllene-derived SOA-coated BC only froze in the homogeneous regime. We attribute the inhibition of IN ability to the filling of the pores on the BC surface by the SOA material coating. OH exposure levels of n-dodecane and β-caryophyllene SOA coating experiments, from an equivalent atmospheric exposure time from 10 to 90 d, did not render significant differences in the IN potential. Our study of selected BC types and sizes suggests that increases in diameter, compactness, and/or surface oxidation of BC particles lead to more efficient IN via the pore condensation freezing (PCF) pathway, and that coatings of common SOA materials can inhibit the formation of ice.

Thymol and cinnamaldehyde are phytogenic feed additives developed to improve gut health and growth performance in poultry and swine. This study evaluated the in vitro immune modulating effects of thymol and cinnamaldehyde blend (TCB) in a porcine gut epithelial cell line (IPEC-J2), with or without cellular damage caused by challenge with lipopolysaccharides. Cytotoxicity, permeability, wound-healing and bacteria adhesion assays were recorded. The expression of cytokines, tight junctions and polymeric immunoglobulin receptor (pIgR) were measured by RT-PCR. The IPEC-J2 cells were cultured in the presence of TCB at concentrations ranging from 1 ng/ml to 1 μg/ml and displayed high viability (>90%). TCB increased barrier integrity (13.8% less in lipopolysaccharide challenge which induced gut epithelial leakage, P

Thymol and cinnamaldehyde are phytogenic feed additives that have been developed to improve gut health, immunity and growth performance in poultry and swine. This study evaluated the immune modulating effects of a thymol and cinnamaldehyde blend (TCB) in the intestinal system of poultry in vitro, using two chicken cell lines, LMH (liver cell line) which has been used to mimic epithelial cell responses, and HD-11 (monocyte/macrophage-like). Cells with high viability (>95%) from established cell lines were cultured in the presence of TCB at concentrations ranging from 1 ng/ml to 100 ng/ml. The viability, transepithelial electrical resistance (TEER) and phagocytic capacity of co-cultured LMH cells, with or without stimulation with lipopolysaccharide (LPS), was subsequently evaluated. The expression of cytokines, chemokines and pattern recognition receptors by HD-11 monocytes/macrophages was measured by RT-PCR and by proteomic analysis. TCB was well tolerated by both cell lines (cell viability >90% after co-culture with TCB at 100 ng/ml for 48 h with or without LPS). Epithelial integrity of LMH cells (as assessed by TEER) was increased by TCB (10 ng/ml) after 4 h incubation, versus untreated controls, and phagocytic capacity of HD-11 cells was increased, in a dose-dependent manner (P

The aerosol–planetary boundary layer (PBL) interaction was proposed as an important mechanism to stabilize the atmosphere and exacerbate surface air pollution. Despite the tremendous progress made in understanding this process, its magnitude and significance still have large uncertainties and vary largely with aerosol distribution and meteorological conditions. In this study, we focus on the role of aerosol vertical distribution in thermodynamic stability and PBL development by jointly using micropulse lidar, sun photometer, and radiosonde measurements taken in Beijing. Despite the complexity of aerosol vertical distributions, cloud-free aerosol structures can be largely classified into three types: well-mixed, decreasing with height, and inverse structures. The aerosol–PBL relationship and diurnal cycles of the PBL height and PM2.5 associated with these different aerosol vertical structures show distinct characteristics. The vertical distribution of aerosol radiative forcing differs drastically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in the heating rate affects the atmospheric buoyancy and stability differently in the three distinct aerosol structures. Absorbing aerosols have a weaker effect of stabilizing the lower atmosphere under the decreasing structure than under the inverse structure. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and can be potentially neutralized by the decreasing structure. Moreover, aerosols can both enhance and suppress PBL stability, leading to both positive and negative feedback loops. This study attempts to improve our understanding of the aerosol–PBL interaction, showing the importance of the observational constraint of aerosol vertical distribution for simulating this interaction and consequent feedbacks.

Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (true positive rate = 97.27%, false positive rate = 5.45%, area under the ROC curve = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible mean on inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also gives a possible experimental analysis direction that the resonant magnetic perturbations delays the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.

SUMMARY: Feed hygienics is of increasing concern for poultry producers. This project included 2 studies to investigate kinetic parameters for inactivation of Salmonella Typhimurium in mash broiler feed heated at various temperatures. In study one, 2- and 5-g feed samples, contained in a sample bag, were inoculated with S. Typhimurium and submerged in a water bath heated to 90°C until internal temperatures of 75°, 80°, and 85°C were reached. In study two, 2-g of sample were inoculated with S. Typhimurium and submerged into a water bath set at 75°, 80°, 85°, 90°, and 95°C heated for 0 to 180 s. Feed sample and water bath temperature were monitored by thermocouples. Thermocouple data demonstrated 2- and 5-g samples achieved a 5-log reduction of S. Typhimurium when internal temperature of feed reached 80°C and 85°C, respectively. D-values for linear and Weibull models were calculated for the 5 temperatures. D-values of the linear model were 6.70, 8.83, 12.05, 13.91, and 24.40 s, and D-values for the Weibull model were 2.27, 3.67, 3.95, 4.68, and 7.63 s when heated to 95°, 90°, 85°, 80°, and 75°C. Individual temperature datasets were further analyzed using GinaFit software. As heating temperature decreased, Double-Weibull and Biphasic models fit all the thermal data, indicating that Salmonella could have generated 2 subpopulations with different thermoresistance. Similar to other thermal inactivation models, this study demonstrates that linear, Weibull, Double-Weibull, and Biphasic models could be used to predict thermal inactivation of Salmonella in mash broiler feed. These data may contribute to establishing feed mill microbiological safety standards and reducing Salmonella contamination in broiler feed.

SUMMARY: Feed hygienics are of ever increasing importance in providing safe feed to animals, and ultimately safe food for consumers. Salmonella has been identified as a major microbial hazard in animal feed that has been linked to illness in both animals and humans. Use of antibiotics has decreased in recent years due to policies and practices of poultry production, increasing opportunities for potential pathogens in feed to affect poultry and poultry products. New feed equipment technology provides an option to combat feed pathogens. For example, hygienisers have been suggested to decrease Salmonella associated with mash feeds due to the ability to maintain conditioned feed temperature for an extended time. Enterococcus faecium (E. faecium) is a non-pathogenic surrogate used to study reduction of Salmonella. The objective of the current study was to compare feed manufacture and Salmonella surrogate (E. faecium ATCC 8459) reduction differences between standard pelleting and more thermally aggressive pelleting utilizing a hygieniser. More thermally aggressive pelleting decreased pellet mill motor load (P = 0.02), increased hot pellet temperature (P = 0.02), and tended to increase pellet durability (P = 0.07). E. faecium ATCC 8459 colonies decreased with standard pelleting relative to inoculated mash and were reduced further with more thermally aggressive pelleting (P

Large uncertainties exist when estimating radiative effects of ambient black carbon (BC) aerosol. Previous studies about the BC aerosol radiative forcing mainly focus on the BC aerosols' mass concentrations and mixing states, while the effects of BC mass size distribution (BCMSD) were not well considered. In this paper, we developed a method of measuring the BCMSD by using a differential mobility analyzer in tandem with an Aethalometer. A comprehensive method of multiple charging corrections was proposed and implemented in measuring the BCMSD. Good agreement was obtained between the BC mass concentration integrated from this system and that measured in the bulk phase, demonstrating the reliability of our proposed method. Characteristics of the BCMSD and corresponding radiative effects were studied based on a field measurement campaign conducted in the North China Plain by using our own measurement system. Results showed that the BCMSD had two modes and the mean peak diameters of the modes were 150 and 503 nm. The BCMSD of the coarser mode varied significantly under different pollution conditions with peak diameter varying between 430 and 580 nm, which gave rise to significant variation in aerosol bulk optical properties. The direct aerosol radiative forcing was estimated to vary by 8.45 % for different measured BCMSDs of the coarser mode, which shared the same magnitude with the variation associated with assuming different aerosol mixing states (10.5 %). Our study reveals that the BCMSD as well as its mixing state in estimating the direct aerosol radiative forcing matters. Knowledge of the BCMSD should be fully considered in climate models.

It is generally believed that field-aligned currents (FACs) and the ring current (RC) are two dominant parts of the inner magnetosphere. However, using the Cluster spacecraft crossing the pre-midnight inner plasma sheet in the latitudinal region between 10 and 30∘ N, it is found that, during intense geomagnetic storms, in addition to FACs and the RC, strong southward and northward currents also exist which should not be FACs because the magnetic field in these regions is mainly along the x–y plane. Detailed investigation shows that both magnetic-field lines (MFLs) and currents in these regions are highly dynamic. When the curvature of MFLs changes direction in the x–y plane, the current also alternatively switches between being southward and northward. To investigate the generation mechanism of the southward and northward current, we employed the analysis of energetic particle flux up to 1 MeV. For energetic particles below 40 keV, observations from Cluster CIS/CODIF (Cluster Ion Spectrometry COmposition and DIstribution Function analyzer) are used. However, for higher-energy particles, the flux is obtained by extrapolations of low-energy particle data through Kappa distribution. The result indicates that the most reasonable cause of these southward and northward currents is the curvature drift of energetic particles.

The study of atmospheric nitrous acid (HONO), which is the primary source of OH radicals, is crucial with respect to understanding atmospheric photochemistry and heterogeneous chemical processes. Heterogeneous NO2 chemistry under haze conditions has been identified as one of the missing sources of HONO on the North China Plain, and also produces sulfate and nitrate. However, controversy exists regarding the various proposed HONO production mechanisms, mainly regarding whether SO2 directly takes part in the HONO production process and what roles NH3 and the pH value play. In this paper, never before seen explosive HONO production was reported and evidence was found – for the first time in field measurements during fog (usually with 4< pH <6) and haze episodes under high relative humidity (pH ≈4) – that NH3 was the key factor that promoted the hydrolysis of NO2, leading to the explosive growth of HONO and nitrate under both high and relatively lower pH conditions. The results also suggest that SO2 plays a minor or insignificant role in HONO formation during fog and haze events, but was indirectly oxidized upon the photolysis of HONO via subsequent radical mechanisms. Aerosol hygroscopicity significantly increased with rapid inorganic secondary aerosol formation, further promoting HONO production as a positive feedback. For future photochemical and aerosol pollution abatement, it is crucial to introduce effective NH3 emission control measures, as NH3-promoted NO2 hydrolysis is a large daytime HONO source, releasing large amounts of OH radicals upon photolysis, which will contribute largely to both atmospheric photochemistry and secondary aerosol formation.

It is argued that heavy-quarks traversing a Quark Gluon Plasma, undergo a multi-stage modification process, similar to the case of light flavors. Such an approach is applied to heavy-quark energy loss, which includes a rare-scattering, multiple emission formalism at momenta large compared to the mass (sensitive only to the transverse diffusion coefficient qˆ), and a single scattering induced emission formalism (Gunion-Bertsch) at momenta comparable to the mass of the quark [sensitive to qˆ, and the longitudinal drag (eˆ) and diffusion (eˆ2) coefficients]. The application of such a multi-stage approach within a 2+1D viscous fluid-dynamical simulation leads to simultaneous agreement with experimental data for the nuclear modification factor of both B and D mesons, as measured by the CMS collaboration at the LHC. The extracted transport coefficients are found to be consistent with those for light flavors. Keywords: Heavy-ion collisions, Quark-gluon-plasma, Heavy-quark, Jet quenching

Abstract The flapping motion of Mercury's magnetotail current sheet is investigated based on the observations of MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER). The typical flapping period in Mercury's magnetotail is much shorter (~13 s), comparing with ~10–20 min at Earth's magnetotail. The magnetic field oscillation amplitude induced by flapping motion is larger near both flanks than that around tail center. Similar to Earth's magnetotail, there are two flapping types existent in Mercury's magnetotail, one is the kink‐like flapping that can propagate as traveling waves, and the other one is the steady flapping that does not propagate. Both flapping types distribute widely in magnetotail. The kink‐like flapping waves propagating either dawnward or duskward can be observed across the entire magnetotail current sheet, which suggests that the energy sources for triggering the kink‐like flapping waves should locate around both tail flanks instead of tail center as is for the Earth's magnetotail.

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens. Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn, stimulate advances in machine learning as well.

The aerosol asymmetry factor (g) is one of the most important factors for assessing direct aerosol radiative forcing. However, little attention has been paid to the measurement and parameterization of g. In this study, the characteristics of g are studied based on field measurements over the North China Plain (NCP) using the Mie scattering theory. The results show that calculated g values for dry aerosol can vary over a wide range (between 0.54 and 0.67). Furthermore, when ambient relative humidity (RH) reaches 90 %, g is significantly enhanced by a factor of 1.2 due to aerosol hygroscopic growth. For the first time, a novel method of calculating g based on measurements from the humidified nephelometer system is proposed. This method can constrain the uncertainty of g to within 2.56 % for dry aerosol populations and 4.02 % for ambient aerosols, providing that aerosol hygroscopic growth is taken into account. Sensitivity studies show that aerosol hygroscopicity plays a vital role in the accuracy of predicting g.

The number concentration of cloud condensation nuclei (CCN) plays a fundamental role in cloud physics. Instrumentations of direct measurements of CCN number concentration (NCCN) based on chamber technology are complex and costly; thus a simple way for measuring NCCN is needed. In this study, a new method for NCCN calculation based on measurements of a three-wavelength humidified nephelometer system is proposed. A three-wavelength humidified nephelometer system can measure the aerosol light-scattering coefficient (σsp) at three wavelengths and the light-scattering enhancement factor (fRH). The Ångström exponent (Å) inferred from σsp at three wavelengths provides information on mean predominate aerosol size, and hygroscopicity parameter (κ) can be calculated from the combination of fRH and Å. Given this, a lookup table that includes σsp, κ and Å is established to predict NCCN. Due to the precondition for the application, this new method is not suitable for externally mixed particles, large particles (e.g., dust and sea salt) or fresh aerosol particles. This method is validated with direct measurements of NCCN using a CCN counter on the North China Plain. Results show that relative deviations between calculated NCCN and measured NCCN are within 30 % and confirm the robustness of this method. This method enables simplerNCCN measurements because the humidified nephelometer system is easily operated and stable. Compared with the method using a CCN counter, another advantage of this newly proposed method is that it can obtain NCCN at lower supersaturations in the ambient atmosphere.

Cold stress is one of the major limitations to crop productivity worldwide. We investigated the effects of multiple gene expression from cold tolerant Capsella bursa-pastoris in transgenic tobacco (Nicotiana tabaccum) plants. We combined CblCE53 and CbCBF into a reconstruct vector by isocaudomers. Plant overexpression of CbICE53 under the stress inducible CbCOR15b promoter and CbCBF under a constitutive promoter showed increased tolerance to both chilling and freezing temperatures in comparison to wild-type plants, according to the electrolyte leakage and relative water content. The expressions of endogenous cold-responsive genes in transgenic tobacco (NtDREB1, NtDREB3, NtERD10a and NtERD10b) were obviously upregulated under normal and low temperature conditions. These results suggest that the CbICE53 + CbCBF transgenic plants showed a much greater cold tolerance as well as no dwarfism and delayed flowering. Thus they can be considered as a potential candidate for transgenic engineering for cold tolerant tobacco.

China experiences frequent and severe haze outbreaks from the beginning of winter. Carbonaceous aerosols are regarded as an essential factor in controlling the formation and evolution of haze episodes. To elucidate the carbon sources of air pollution, source apportionment was conducted using radiocarbon (14C) and unique molecular organic tracers. Daily 24 h PM2. 5 samples were collected continuously from October 2013 to November 2013 in 10 Chinese cities. The 14C results indicated that non-fossil-fuel (NF) emissions were predominant in total carbon (TC; average = 65 ± 7 %). Approximately half of the EC was derived primarily from biomass burning (BB) (average = 46 ± 11 %), while over half of the organic carbon (OC) fraction comprised NF (average = 68 ± 7 %). On average, the largest contributor to TC was NF-derived secondary OC (SOCnf), which accounted for 46 ± 7 % of TC, followed by SOC derived from fossil fuels (FF) (SOCf; 16 ± 3 %), BB-derived primary OC (POCbb; 13 ± 5 %), POC derived from FF (POCf; 12 ± 3 %), EC derived from FF (ECf; 7 ± 2 %) and EC derived from BB (ECbb; 6 ± 2 %). The regional background carbonaceous aerosol composition was characterized by NF sources; POCs played a major role in northern China, while SOCs contributed more in other regions. However, during haze episodes, there were no dramatic changes in the carbon source or composition in the cities under study, but the contribution of POC from both FF and NF increased significantly.

Accurate high-resolution estimates of precipitation are vital to improving the understanding of basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing the spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the annual precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation, and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous areas of the Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in the Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and the TRMM (Tropical Rainfall Measuring Mission) precipitation product.

Fine carbonaceous aerosols (CAs) is the key factor influencing the currently filthy air in megacities in China, yet few studies simultaneously focus on the origins of different CAs species using specific and powerful source tracers. Here, we present a detailed source apportionment for various CAs fractions, including organic carbon (OC), water-soluble OC (WSOC), water-insoluble OC (WIOC), elemental carbon (EC) and secondary OC (SOC) in the largest cities of North (Beijing, BJ) and South China (Guangzhou, GZ), using the measurements of radiocarbon and anhydrosugars. Results show that non-fossil fuel sources such as biomass burning and biogenic emission make a significant contribution to the total CAs in Chinese megacities: 56 ± 4 in BJ and 46 ± 5 % in GZ, respectively. The relative contributions of primary fossil carbon from coal and liquid petroleum combustions, primary non-fossil carbon and secondary organic carbon (SOC) to total carbon are 19, 28 and 54 % in BJ, and 40, 15 and 46 % in GZ, respectively. Non-fossil fuel sources account for 52 in BJ and 71 % in GZ of SOC, respectively. These results suggest that biomass burning has a greater influence on regional particulate air pollution in North China than in South China. We observed an unabridged haze bloom-decay process in South China, which illustrates that both primary and secondary matter from fossil sources played a key role in the blooming phase of the pollution episode, while haze phase is predominantly driven by fossil-derived secondary organic matter and nitrate.

Dramatic changes in hydrological factors in the Weihe River basin are analysed. These changes have exacerbated ecological problems and caused severe water shortages for agriculture, industries and the human population in the region, but their drivers are uncertain. The Mann-Kendall test, accumulated departure analysis, sequential clustering and the sliding t-test methods were used to identify the causes of changes in precipitation and runoff in the Weihe basin. Change-points were identified in the precipitation and runoff records for all sub-catchments. For runoff, the change in trend was most pronounced during the 1990s, whereas changes in precipitation were more prominent earlier. The results indicate that human activities have had a greater impact than climate change on the hydrology of the Weihe basin. These findings have significant implications for the establishment of effective strategies to counter adverse effects of hydrological changes in the catchment.

Changing runoff patterns can have profound effects on the economic development of river basins. To assess the impact of human activity on runoff in the Weihe River basin, principal component analysis (PCA) was applied to a set of 17 widely used indicators of economic development to construct general combined indicators reflecting different types of human activity. Grey relational analysis suggested that the combined indicator associated with agricultural activity was most likely to have influenced the changes in runoff observed within the river basin during 1994–2011. Curve fitting was then performed to characterize the relationship between the general agricultural indicator and the measured runoff, revealing a reasonably high correlation (R2 = 0.393) and an exponential relationship. Finally, a sensitivity analysis was performed to assess the influence of the 17 individual indicators on the measured runoff, confirming that indicators associated with agricultural activity had profound effects whereas those associated with urbanization had relatively little impact.

Watershed-scale hydrological and biogeochemical models are usually discretized at resolutions coarser than where significant heterogeneities in topography, abiotic factors (e.g., soil properties), and biotic (e.g., vegetation) factors exist. Here we report on a method to use fine-scale (220 m grid cells) hydrological model predictions to build reduced-order models of the statistical properties of near-surface soil moisture at coarse resolution (25 times coarser, ~7 km). We applied a watershed-scale hydrological model (PAWS-CLM4) that has been previously tested in several watersheds. Using these simulations, we developed simple, relatively accurate (R2 ~0.7–0.8), reduced-order models for the relationship between mean and higher-order moments of near-surface soil moisture during the nonfrozen periods over five years. When applied to transient predictions, soil moisture variance and skewness were relatively accurately predicted (R2 0.7–0.8), while the kurtosis was less accurately predicted (R2 ~0.5). We also tested 16 system attributes hypothesized to explain the negative relationship between soil moisture mean and variance toward the wetter end of the distribution and found that, in the model, 59% of the variance of this relationship can be explained by the elevation gradient convolved with mean evapotranspiration. We did not find significant relationships between the time rate of change of soil moisture variance and covariances between mean moisture and evapotranspiration, drainage, or soil properties, as has been reported in other modeling studies. As seen in previous observational studies, the predicted soil moisture skewness was predominantly positive and negative in drier and wetter regions, respectively. In individual coarse-resolution grid cells, the transition between positive and negative skewness occurred at a mean soil moisture of ~0.25–0.3. The type of numerical modeling experiments presented here can improve understanding of the causes of soil moisture heterogeneity across scales, and inform the types of observations required to more accurately represent what is often unresolved spatial heterogeneity in regional and global hydrological models.

We use proxy data and modeled data from 1000 year model simulations with a variety of climate forcings to examine the occurrence of severe event of persistent drought over eastern China during the last millennium and diagnose the mechanisms. Results show that the model was able to roughly simulate most of these droughts over the study area during the last millennium such as those that occurred during the periods of 1123–1152, 1197–1223, 1353–1363, 1428–1449, 1479–1513, and 1632–1645. Our analyses suggest that these six well-captured droughts may caused by the East Asian summer monsoon (EASM) weakening. Study on the wavelet transform and spectral analysis reveals these events occurred all at the statistically significant 15–35-year timescale. A modeled data intercomparison suggests the possibility that solar activity may be the primary driver in the occurrence of the 1129–1144, 1354–1365, 1466–1491 and 1631–1648 droughts as identified by the model. However another possibility that these events may be related to internal variability cannot be excluded. Although the El Niño–Southern Oscillation (ENSO) plays an important role in monsoon variability, a temporally consistent relationship between the droughts and SST pattern in the Pacific Ocean could not be found either in the modeled or proxy data. Our analyses also indicate that large volcanic eruptions play a role as an amplifier in the drought of 1631–1648 and caused the droughts of 1830–1853 and 1958–1976, which was identified by the model.

With the certain regulation of unified real estate registration taken by the Property Law and the step-by-step advance of simultaneous development in urban and rural in China, it is the premise and foundation to clearly specify property rights and their relations in promoting the integrated management of urban and rural land. This paper aims at developing a cadastral domain model oriented at unified real estate registration of China from the perspective of legal and spatial, which set up the foundation for unified real estate registration, and facilitates the effective interchange of cadastral information and the administration of land use. The legal cadastral model is provided based on the analysis of gap between current model and the demand of unified real estate registration, which implies the restrictions between different rights. Then the new cadastral domain model is constructed based on the legal cadastral domain model and CCDM (van Oosterom et al., 2006), which integrate real estate rights of urban land and rural land. Finally, the model is validated by a prototype system. The results show that the model is applicable for unified real estate registration in China.

The Kelvin-Helmholtz Instability (KHI) can drive waves at the magnetopause. These waves can grow to form rolled-up vortices and facilitate transfer of plasma into the magnetosphere. To investigate the persistence and frequency of such waves at the magnetopause we have carried out a survey of all Double Star 1 magnetopause crossings, using a combination of ion and magnetic field measurements. Using criteria originally used in a Geotail study made by Hasegawa et al. (2006) (forthwith referred to as H2006), 17 candidate events were identified from the entire TC-1 mission (covering ~623 orbits where the magnetopause was sampled), a majority of which were on the dayside of the terminator. The relationship between density and shear velocity was then investigated, to identify the predicted signature of a rolled up vortex from H2006 and all 17 events exhibited some level of rolled up behavior. The location of the events had a clear dawn-dusk asymmetry, with 12 (71%) on the post noon, dusk flank suggesting preferential growth in this region.

A study of the interaction of solar wind magnetic field rotations with the Earth's magnetosphere is performed. For this event there is, for the first time, a full coverage over the dayside magnetosphere with multiple (multi)spacecraft missions from dawn to dusk, combined with ground magnetometers, radar and an auroral camera, this gives a unique coverage of the response of the Earth's magnetosphere. After a long period of southward IMF Bz and high dynamic pressure of the solar wind, the Earth's magnetosphere is eroded and compressed and reacts quickly to the turning of the magnetic field. We use data from the solar wind monitors ACE and Wind and from magnetospheric missions Cluster, THEMIS, DoubleStar and Geotail to investigate the behaviour of the magnetic rotations as they move through the bow shock and magnetosheath. The response of the magnetosphere is investigated through ground magnetometers and auroral keograms. It is found that the solar wind magnetic field drapes over the magnetopause, while still co-moving with the plasma flow at the flanks. The magnetopause reacts quickly to IMF Bz changes, setting up field aligned currents, poleward moving aurorae and strong ionospheric convection. Timing of the structures between the solar wind, magnetosheath and the ground shows that the advection time of the structures, using the solar wind velocity, correlates well with the timing differences between the spacecraft. The reaction time of the magnetopause and the ionospheric current systems to changes in the magnetosheath Bz seem to be almost immediate, allowing for the advection of the structure measured by the spacecraft closest to the magnetopause.

During April to July 2007 a combination of 10 spacecraft provided simultaneous monitoring of the dayside magnetopause across a wide range of local times. The array of four Cluster spacecraft, separated at large distances (10 000 km), were traversing the dawn-side magnetopause at high and low latitudes; the five THEMIS spacecraft were often in a 4 + 1 grouped configuration, traversing the low latitude, dusk-side magnetosphere, and the Double star, TC-1 spacecraft was in an equatorial orbit between the local times of the THEMIS and Cluster orbits. We show here a number of near simultaneous conjunctions of all 10 spacecraft at the magnetopause. One conjunction identifies an extended magnetic reconnection X-line, tilted in the low latitude, sub-solar region, which exists together with active anti-parallel reconnection sites extending to locations on the dawn-side flank. Oppositely moving FTE's are observed on all spacecraft, consistent with the initially strong IMF By conditions and the comparative locations of the spacecraft both dusk-ward and dawn-ward of noon. Comparison with other conjunctions of magnetopause crossings, which are also distributed over wide local times, supports the result that reconnection activity may occur at many sites simultaneously across the sub-solar and flank magnetopause, but linked to the large scale (extended) configuration of the merging line; broadly depending on IMF orientation. The occurrence of MR therefore inherently follows a "component" driven scenario irrespective of the guide field conditions. Some conjunctions allow the global magnetopause response to IMF changes to be observed and the distribution of spacecraft can directly confirm its shape, motion and deformation at local noon, dawn and dusk-side, simultaneously.

The relationship between the average structure of the inner magnetospheric large-scale electric field and geomagnetic activity levels has been investigated by Double Star TC-1 data for radial distances ρ between 4.5 RE and 12.5 RE and MLT between 18:00 h and 06:00 h from July to October in 2004 and 2005. The sunward component of the electric field decreases monotonically as ρ increases and approaches zero as the distance off the Earth is greater than 10 RE. The dawn-dusk component is always duskward. It decreases at about 6 RE where the ring current is typically observed to be the strongest and shows strong asymmetry with respect to the magnetic local time. Surprisingly, the average electric field obtained from TC-1 for low activity is almost comparable to that observed during moderate activity, which is always duskward at the magnetotail (8 RE~12 RE).

The magnetic field in many regions of magnetosphere has a complex topological structure. As a parameter to measure the topological complexity, the concept of magnetic helicity is a useful tool in magnetospheric physics. Here we present a case study of magnetic helicity in the flux rope (FR) in the near-Earth plasma sheet (PS) based on the in-situ observation from THEMIS for the first time. With the help of the Grad-Shafranov reconstruction technique, we determine the spatial distribution of magnetic field and evaluate the magnetic helicity in the flux rope. The conservation of magnetic helicity during multiple X-line reconnections and the transport of magnetic helicity between different magnetic field configurations are also discussed. The further application of helicity in magnetosphere will provide us more knowledge about the topologic property of the magnetic fields there and more attention should be paid to that.

Inorganic phosphorus (SRP) concentrations in the subtropical North Atlantic are some of the lowest in the global ocean and have been hypothesized to constrain primary production. Based upon data from several transect cruises in this region, it has been hypothesized that dissolved organic phosphorus (DOP) supports a significant fraction of primary production in the subtropical North Atlantic. In this study, a time-series of phosphorus biogeochemistry is presented for the Bermuda Atlantic Time-series Study site, including rates of phosphorus export. Most parameters have a seasonal pattern, although year-over-year variability in the seasonal pattern is substantial, likely due to differences in external forcing. Suspended particulate phosphorus exhibits a seasonal maximum during the spring bloom, despite the absence of a seasonal peak in SRP. However, DOP concentrations are at an annual maximum prior to the winter/spring bloom and decline over the course of the spring bloom while whole community alkaline phosphatase activities are highest. As a result of DOP bioavailability, the growth of particles during the spring bloom occurs in Redfield proportions, though particles exported from the euphotic zone show rapid and significant remineralization of phosphorus within the first 50 m below the euphotic zone. Based upon DOP data from transect cruises in this region, the southward cross gyral flux of DOP is estimated to support ~25% of annual primary production and ~100% of phosphorus export. These estimates are consistent with other research in the subtropical North Atlantic and reinforce the hypothesis that while the subtropics may be phosphorus stressed (a physiological response to low inorganic phosphorus), utilization of the DOP pool allows production and accumulation of microbial biomass at Redfield proportions.